Sensor kits at edge devices for monitoring and managing industrial settings

ABSTRACT

A variety of kits are provided that are configured with components, systems and methods for monitoring various industrial settings, including kits with self-configuring sensor networks, communication gateways, and automatically configured back end systems.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a bypass continuation of International ApplicationNo. PCT/US2019/059088, filed on Oct. 31, 2019, which claims priority toU.S. Provisional Patent Application No. 62/791,878 filed on Jan. 13,2019, U.S. Provisional Patent Application No. 62/827,166 filed on Mar.31, 2019, U.S. Provisional Patent Application No. 62/869,011 filed onJun. 30, 2019, and U.S. Provisional Patent Application No. 62/914,998filed on Oct. 14, 2019, each entitled METHODS, SYSTEMS, KITS, ANDAPPARATUSES FOR MONITORING INDUSTRIAL SETTINGS. Each of theabove-identified applications is hereby incorporated by reference in itsentirety as if fully set forth herein.

FIELD

The present disclosure relates to various configurations of Internet ofThings (IoT) systems in conveniently deployed kits that monitor ormanage industrial settings using various configurations of sensors, edgecomputing devices, networking systems, and artificial intelligence.

BACKGROUND

The Internet of Things (IoT) is a network of connected devices, systems,components, services, programs, vehicles, appliances, machines, andother electronic items that communicate via a set of communicationnetworks and communication interfaces and protocols. While much of thedevelopment in the IoT space has centered on consumer products, such aswearable devices, home monitoring systems, smart appliances, and thelike, there are many industrial applications for IoT devices andsystems, including embodiments described throughout this disclosure andin the documents incorporated herein. For example, IoT sensors can beused to monitor industrial facilities, such as factories, refineries,oil and gas fields, manufacturing lines, energy production facilities,mining environments, and the like, as well as the many machines andsystems disposed in such environments. While machines may includeembedded sensors and instrumentation, such as onboard diagnosticsystems, many machines do not have such embedded sensors, and othersonly have a limited set of sensors; accordingly, a need and anopportunity exist for vastly more data collection, such as via thelocation (which may be temporary (such as with portable or mobile datacollectors as described in documents incorporated by reference, or bydrones, autonomous vehicles, or the like), semi-permanent (such as withmodular interfaces for convenient connection and disconnection), orpermanent) of large numbers of heterogeneous sensors of various typeson, in or around machines in industrial environments.

There are a number of issues, however, that arise in the Industrial IoTsetting. For example, while many industrial IoT devices may beconfigured to communicate using cellular protocols, such as the 3G, 4G,LTE or 5G communication protocols, those protocols may not be nativelywell suited for communication in the industrial setting, as heavymachinery and thick dense structures may adversely affect communicationbetween devices. Wi-Fi systems may also provide network connectionswithin facilities; however, Wi-Fi systems may also experience challengesdue to the adverse physical environments involved in industrialsettings. For example, Wi-Fi systems are not typically well designed tocommunicate through obstructions, such as slabs of concrete or brick.Also, many devices in an industrial setting may be mobile, such thatWi-Fi and cellular systems have difficulty resolving which devices arecommunicating at a given time.

Another issue that may arise is related to bandwidth. As hundreds orthousands of sensors may be placed in an area to be monitored (e.g.,factory, assembly line, oil field, etc.), and those sensors may capturemultiple readings every second, the amount of data that is beingcollected may put a strain on the computing resources of even the mostrobust computing systems. A need exists for methods and systems thataddress challenges of efficient and effective bandwidth utilization.

Another issue is security. IoT devices can be perceived as securityrisks when the devices are connected to computer networks, such as onesused to operate mission critical machines. IoT devices have historicallyexperienced security vulnerabilities and have frequently been points ofattack on networks and devices.

Concerns about bandwidth, reliability, latency and/or security may deterorganizations from integrating IoT sensor systems into their industrialenvironments and computer networks. A need exists for systems thatprovide the benefits of the IoT while addressing networking needs andsecurity risks.

Another challenge for organizations considering IoT deployments is thatsuch deployments require sophisticated integration of IoT devices withnetworking systems and with platforms (e.g., cloud platforms) whereanalysis of IoT-collected data is performed and where both human andautomated controls are provided for industrial settings. Organizationsmay lack the range of expertise or available staff to undertakeeffective IoT integrations. A need exists for simplified deploymentsystems that offer the benefits of the IoT.

SUMMARY

Provided herein are methods and systems for monitoring and managingindustrial settings, including through a variety of configurable kitsthat provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring and managing industrial settingswhile mitigating issues of complexity, integration, bandwidth, latencyand security. The practical implementation of an IoT solution mayinclude a set of components that may comprise an appropriate set ofsensors each configured for various respective industrial settings, aset of communication devices, a set of edge computing devices and a setof communication capabilities (including various protocols, ports,gateways, connectors, interfaces and the like) that collectively provideautomatically configured and/or pre-configured processing andtransmission of sensor data from the sensor kits to a set of backendsystems (e.g., cloud-deployed systems or on-premises systems) viaappropriate protocols, and a set of backend systems that areautomatically configured and/or preconfigured to provide monitoringand/or management information to owners and operators of industrialsettings from the particular sensor kits that are registered to theirindustrial settings. As used herein “set” may include a set with asingle member. References to “monitoring” and/or to “management” shouldbe understood, except where context indicates otherwise, to encompassvarious actions or activities that may benefit from the informationshared via the IoT, such as monitoring machine performance, reporting onstatus, states, or conditions, managing states, conditions, parameters,undertaking remote control, supporting autonomous functions that dependon status or state information, supporting analytics, supportingself-configuration, supporting artificial intelligence, supportingmachine learning, and the like.

According to some embodiments of the present disclosure, a sensor kitconfigured for monitoring an industrial setting is disclosed. Inembodiments, the sensor kit includes an edge device and a plurality ofsensors, i.e., a set of sensors, that capture sensor data and transmitthe sensor data via a self-configuring sensor kit network. The pluralityof sensors includes one or more sensors of a first sensor type and oneor more sensors of a second sensor type. At least one sensor of theplurality of sensors includes a sensing component that captures sensormeasurements and outputs instances of sensor data; a processing unitthat generates reporting packets based on one or more instances ofsensor data and outputs the reporting packets, wherein each reportingpacket includes routing data and one or more instances of sensor data;and a communication device configured to receive reporting packets fromthe processing unit and to transmit the reporting packets to the edgedevice via the self-configuring sensor kit network in accordance with afirst communication protocol. The edge device includes a communicationsystem having: a first communication device that receives reportingpackets from the plurality of sensors via the self-configuring sensorkit network and a second communication device that transmits sensor kitpackets to a backend system via a public network. The edge devicefurther includes a processing system having one or more processors thatexecute computer-executable instructions that cause the processingsystem to: receive the reporting packets from the communication system;perform one or more edge operations on the instances of sensor data inthe reporting packets; generate the sensor kit packets based on theinstances of sensor data, wherein each sensor kit packet includes atleast one instance of sensor data; and output the sensor kits packets tothe communication system, wherein the communication system transmits thereporting packets to the backend system via the public network.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In some embodiments, the edge device further includes one or morestorage devices that store a sensor data store that stores instances ofsensor data captured by the plurality of sensors of the sensor kit.

In some embodiments, the edge device further includes one or morestorage devices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial setting and/orthe industrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors. In some of these embodiments, performing one or more edgeoperations includes: generating a feature vector based on one or moreinstances of sensor data received from one or more sensors of theplurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the industrialsetting or the industrial setting and a degree of confidencecorresponding to the prediction or classification; and selectivelyencoding the one or more instances of sensor data prior to transmissionto the backend system based on the condition or prediction. In some ofthese embodiments, selectively encoding the one or more instances ofsensor data includes: compressing the one or more instances of sensordata using a lossy codec in response to obtaining one or morepredictions or classifications relating to conditions of respectiveindustrial components of the industrial setting and the industrialsetting that collectively indicate that there are likely no issuesrelating to any industrial component of the industrial setting and theindustrial setting. In some of these embodiments, compressing the one ormore instances of sensor data using the lossy codec includes:normalizing the one or more instances of sensor data into respectivepixel values; encoding the respective pixel values into a video frame;and compressing a block of video frames using the lossy codec, whereinthe lossy codec is a video codec and the block of video frames includesthe video frame and one or more other video frames that includenormalized pixel values of other instances of sensor data. In someembodiments, selectively encoding the one or more instances of sensordata includes compressing the one or more instances of sensor data usinga lossless codec in response to obtaining a prediction or classificationrelating to a condition of a particular industrial component or theindustrial setting that indicates that there is likely an issue relatingto the particular industrial component or the industrial setting. Insome embodiments, selectively encoding the one or more instances ofsensor data includes refraining from compressing the one or moreinstances of sensor data in response to obtaining a prediction orclassification relating to a condition of a particular industrialcomponent or the industrial setting that indicates that there is likelyan issue relating to the particular industrial component or theindustrial setting. In some embodiments, performing one or more edgeoperations includes: generating a feature vector based on one or moreinstances of sensor data received from one or more sensors of theplurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the industrialsetting or the industrial setting and a degree of confidencecorresponding to the prediction or classification; and selectivelystoring the one or more instances of sensor data in a storage device ofthe edge device based on the prediction or classification. In someembodiments, selectively storing the one or more instances of sensordata includes in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting, storing the one or more instances of sensor data in the storagedevice with an expiry, such that the one or more instances of sensordata are purged from the storage device in accordance with the expiry.In some embodiments, selectively storing the one or more instances ofsensor data includes in response to obtaining a prediction orclassification relating to a condition of a particular industrialcomponent or the industrial setting that indicates that there is likelyan issue relating to the particular industrial component or theindustrial setting, storing the one or more instances of sensor data inthe storage device indefinitely.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is a meshnetwork such that the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors, and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitfurther includes one or more collection devices configured to receivereporting packets from one or more sensors of the plurality of sensorsand route the reporting packets to the edge device.

In embodiments, the self-configuring sensor kit network is a ringnetwork that communicates using a serial data protocol.

In embodiments, the sensor kit network is a mesh network.

In embodiments, at least one of the sensors in the sensor kit network isa multi-axis vibration sensor.

In embodiments, the edge device includes a rule-based network protocoladaptor for selecting a network protocol by which to send sensor kitpackets via the public network.

According to some embodiments of the present disclosure, a method formonitoring an industrial setting using a sensor kit having a pluralityof sensors and an edge device including a processing system isdisclosed. In embodiments, the method includes receiving, by theprocessing system, reporting packets from one or more respective sensorsof the plurality of sensors, wherein each reporting packet is sent froma respective sensor and indicates sensor data captured by the respectivesensor; performing, by the processing system, one or more edgeoperations on one or more instances of sensor data received in thereporting packets; generating, by the processing system, one or moresensor kit packets based on the instances of sensor data, wherein eachsensor kit packet includes at least one instance of sensor data; andoutputting, by the processing system, the sensor kit packets to abackend system via a public network. In some embodiments, the reportingpackets received from one or more respective sensors of the plurality ofsensors include a sensor identifier of the respective sensor. Inembodiments, receiving the reporting packets from the one or morerespective sensors is performed using a first communication deviceimplementing a first communication protocol and outputting the sensorkit packets to the backend system is performed using a secondcommunication device implementing a second communication protocol. Insome embodiments, the second communication device is a satelliteterminal device, and outputting the sensor kit packets includestransmitting the sensor kit packets to a satellite using the satelliteterminal device, wherein the satellite routes the sensor kit packets tothe public network. In embodiments, outputting the sensor kit packets toa backend system includes transmitting the sensor kit packets to agateway device of the sensor kit. In some embodiments, transmitting thesensor kit packets to the gateway device includes transmitting thesensor kit packets to the gateway via a wired communication link betweenthe edge device and the gateway device. In embodiments, the gatewaydevice includes a satellite terminal device that is configured totransmit the sensor kit packets to a satellite that routes the sensorkits to the public network. In some embodiments, the gateway deviceincludes a cellular chipset that is pre-configured to transmit sensorkit packets to a cellphone tower of a preselected cellular provider. Inembodiments, the method further includes storing, by one or more storagedevices of the edge device, a model data store that stores one or moremachine-learned models. In some embodiments, the one or moremachine-learned models are trained to predict or classify a condition ofan industrial component of the industrial setting and/or of theindustrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors.

In some embodiments performing one or more edge operations includesgenerating a feature of vector based on one or more instances of sensordata received from one or more sensors of the plurality of sensors;inputting the feature vector to a machine-learned model of the one ormore machine-learned models to obtain a prediction or classificationrelating to a condition of a particular industrial component of theindustrial setting or the industrial setting and a degree of confidencecorresponding to the prediction or classification; and selectivelyencoding the one or more instances of sensor data prior to transmissionto the backend system based on the condition or prediction. In someembodiments, selectively encoding the one or more instances of sensordata includes: compressing the one or more instances of sensor datausing a lossy codec in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting. In embodiments, compressing the one or more instances of sensordata using the lossy codec includes: normalizing the one or moreinstances of sensor data into respective pixel values; encoding therespective pixel values into a video frame; and compressing a block ofvideo frames using the lossy codec, wherein the lossy codec is a videocodec and the block of video frames includes the video frame and one ormore other video frames that include normalized pixel values of otherinstances of sensor data. In some embodiments, selectively encoding theone or more instances of sensor data includes compressing the one ormore instances of sensor data using a lossless codec in response toobtaining a prediction or classification relating to a condition of aparticular industrial component or the industrial setting that indicatesthat there is likely an issue relating to the particular industrialcomponent or the industrial setting. In embodiments, selectivelyencoding the one or more instances of sensor data includes refrainingfrom compressing the one or more instances of sensor data in response toobtaining a prediction or classification relating to a condition of aparticular industrial component or the industrial setting that indicatesthat there is likely an issue relating to the particular industrialcomponent or the industrial setting.

In some embodiments, performing one or more edge operations includes:generating a feature vector based on one or more instances of sensordata received from one or more sensors of the plurality of sensors;inputting the feature vector to the machine-learned model to obtain aprediction or classification relating to a condition of a particularindustrial component of the industrial setting or the industrial settingand a degree of confidence corresponding to the prediction orclassification; and selectively storing the one or more instances ofsensor data in a storage device of the edge device based on theprediction or classification. In embodiments, selectively storing theone or more instances of sensor data includes storing the one or moreinstances of sensor data in the storage device with an expiry such thatthe one or more instances of sensor data are purged from the storagedevice in accordance with the expiry, wherein storing the one or moreinstances of sensor data in the storage device with an expiry isperformed in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting. In some embodiments, selectively storing the one or moreinstances of sensor data includes storing the one or more instances ofsensor data in the storage device indefinitely in response to obtaininga prediction or classification relating to a condition of a particularindustrial component or the industrial setting that indicates that thereis likely an issue relating to the particular industrial component orthe industrial setting.

In some embodiments, the method further includes: capturing, by asensing component of a sensor of the plurality of sensors, sensormeasurements; generating, by a processor of the sensor, one or morereporting packets based on the captured sensor measurements; andtransmitting, by a communication unit of the sensor, the one or morereporting packets to the edge device via a self-configuring sensor kitnetwork. In some of these embodiments, the method further includesinitiating, by the processing system, configuration of theself-configuring sensor kit network, wherein the self-configuring sensorkit network is a star network. In some embodiments, the reportingpackets are received directly from respective sensors using ashort-range communication protocol. In embodiments, the method furtherincludes initiating, by the processing system, configuration of theself-configuring sensor kit network, wherein the self-configuring sensorkit network is a mesh network. In some embodiments, the method furtherincludes: establishing, by the communication device of each sensor ofthe plurality of sensors, a communication channel with at least oneother sensor of the plurality of sensors; receiving, by the at least onesensor of the plurality of sensors, instances of sensor data from one ormore other sensors of the plurality of sensors; and routing, by the atleast one sensor of the plurality of sensors, the received instances ofthe sensor data towards the edge device via the mesh network.

In some embodiments, the self-configuring sensor kit network is ahierarchical network and the sensor kit includes one or more collectiondevices that participate in the hierarchical network. In some of theseembodiments, the method further includes receiving, by a collectiondevice of the one or more collection devices, reporting packets from aset of sensors of the plurality of sensors that communicate with thecollection device using a first short-range communication protocol; androuting, by the one or more collection devices, the reporting packets tothe edge device using one of the first short-range communicationprotocol or a second short-range communication protocol that isdifferent than the second-range communication protocol.

In some embodiments, the edge device includes a rule-based networkprotocol adaptor. In some of these embodiments, the method furtherincludes: selecting, by the rule-based network protocol adaptor, anetwork protocol; and sending, by the edge device, sensor kit packets bythe network protocol via the public network.

In some embodiments, the plurality of sensors includes a first set ofsensors of a first sensor type and a second set of sensors of a secondsensor type.

According to some embodiments of the present disclosure, a sensor kitconfigured for monitoring an industrial setting is disclosed. Inembodiments, the sensor kit includes an edge device and a plurality ofsensors that capture sensor data and transmit the sensor data via aself-configuring sensor kit network. The plurality of sensors includesone or more sensors of a first sensor type and one or more sensors of asecond sensor type. At least one sensor of the plurality of sensorsincludes a sensing component that captures sensor measurements andoutputs instances of sensor data; a processing unit that generatesreporting packets based on one or more instances of sensor data andoutputs the reporting packets, wherein each reporting packet includesrouting data and one or more instances of sensor data; and acommunication device configured to receive reporting packets from theprocessing unit and to transmit the reporting packets to the edge devicevia the self-configuring sensor kit network in accordance with a firstcommunication protocol. The edge device includes one or more storagedevices that store a model data store that stores a plurality ofmachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial setting or of theindustrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors. The edge device further includes a communication system thatreceives reporting packets from the plurality of sensors via theself-configuring sensor kit network using a first communication protocoland that transmits sensor kit packets to a backend system via a publicnetwork using a second communication protocol that is different from thefirst communication protocol. The edge device further includes aprocessing system having one or more processors that executecomputer-executable instructions that cause the processing system to:receive the reporting packets from the communication system; generate aset of feature vectors based on one or more respective instances ofsensor data received in the reporting packets; for each respectivefeature vector, input the respective feature vector into a respectivemachine-learned model that corresponds to the feature vector to obtain arespective prediction or classification relating to a condition of arespective industrial component of the industrial setting or theindustrial setting and a degree of confidence corresponding to therespective prediction or classification; selectively encode the one ormore instances of sensor data prior to transmission to the backendsystem based on the respective predictions or classifications outputtedby the machine-learned models in response to the respective featurevector to obtain one or more sensor kit packets; and output the sensorkits packets to the communication system, wherein the communicationsystem transmits the reporting packets to the backend system via thepublic network.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In embodiments, the one or more storage devices that store a sensor datastore that stores instances of sensor data captured by the plurality ofsensors of the sensor kit.

In embodiments, selectively encoding the one or more instances of sensordata includes, in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting, compressing the one or more instances of sensor data using alossy codec. In some embodiments, compressing the one or more instancesof sensor data using the lossy codec includes: normalizing the one ormore instances of sensor data into respective pixel values; encoding therespective pixel values into a video frame; and compressing a block ofvideo frames using the lossy codec, wherein the lossy codec is a videocodec and the block of video frames includes the video frame and one ormore other video frames that include normalized pixel values of otherinstances of sensor data. In some of these embodiments, selectivelyencoding the one or more instances of sensor data includes: in responseto obtaining a prediction or classification relating to a condition of aparticular industrial component or the industrial setting that indicatesthat there is likely an issue relating to the particular industrialcomponent or the industrial setting, compressing the one or moreinstances of sensor data using a lossless codec.

In some embodiments, selectively encoding the one or more instances ofsensor data includes: in response to obtaining a prediction orclassification relating to a condition of a particular industrialcomponent or the industrial setting that indicates that there is likelyan issue relating to the particular industrial component or theindustrial setting, refraining from compressing the one or moreinstances of sensor data.

In embodiments, the computer-executable instructions further cause theone or more processors of the edge device to selectively store the oneor more instances of sensor data in the one or more storage devices ofthe edge device based on the respective predictions or classifications.In some of these embodiments, selectively storing the one or moreinstances of sensor data includes, in response to obtaining one or morepredictions or classifications relating to conditions of respectiveindustrial components of the industrial setting and the industrialsetting that collectively indicate that there are likely no issuesrelating to any industrial component of the industrial setting and theindustrial setting, storing the one or more instances of sensor data inthe storage device with an expiry, such that the one or more instancesof sensor data are purged from the storage device in accordance with theexpiry. In some embodiments, selectively storing the one or moreinstances of sensor data includes, in response to obtaining a predictionor classification relating to a condition of a particular industrialcomponent or the industrial setting that indicates that there is likelyan issue relating to the particular industrial component or theindustrial setting, storing the one or more instances of sensor data inthe storage device indefinitely.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In some embodiments, the self-configuring sensor kit network is a meshnetwork such that: the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors, and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitincludes one or more collection devices configured to receive reportingpackets from one or more sensors of the plurality of sensors and routethe reporting packets to the edge device.

According to some embodiments of the present disclosure, a method formonitoring an industrial setting using a sensor kit having a pluralityof sensors and an edge device including a processing system isdisclosed. The method includes: receiving, by the processing system,reporting packets from one or more respective sensors of the pluralityof sensors, wherein each reporting packet includes routing data and oneor more instances of sensor data; generating, by the processing system,a set of feature vectors based on one or more respective instances ofsensor data received in the reporting packets; inputting, by theprocessing system, each respective feature vector into a respectivemachine-learned model of a plurality of machine-learned models that areeach trained to predict or classify a respective condition of anindustrial component of the industrial setting or of the industrialsetting based on a set of features that are derived from instances ofsensor data captured by one or more of the plurality of sensors;obtaining, by the processing system, a respective prediction orclassification and a degree of confidence corresponding to therespective prediction or classification from each respectivemachine-learned model based on the respective feature vector inputtedinto the respective machine-learned model; selectively encoding, by theprocessing system, the one or more instances of sensor data based on therespective prediction or classification to obtain one or more sensor kitpackets; and transmitting, by the processing system, the sensor kitpackets to a backend system via a public network. In some embodiments,the sensor kit includes a gateway device configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device. In embodiments, the gateway deviceincludes a satellite terminal device that transmits the sensor kitpackets to a satellite that routes the sensor kit packets to the publicnetwork. In some embodiments, the gateway device includes a cellularchipset that transmits the sensor kit packets to a cellphone tower of apreselected cellular provider. In embodiments, receiving the reportingpackets from the one or more respective sensors is performed using afirst communication device implementing a first communication protocoland transmitting the sensor kit packets to the backend system isperformed using a second communication device implementing a secondcommunication protocol. In some embodiments, the second communicationdevice of the edge device is a satellite terminal device andtransmitting the sensor kit packets to the backend system includestransmitting, by the satellite terminal device, the sensor kit packetsto a satellite that routes the sensor kit packets to the public network

In some embodiments, the method further includes compressing, by theprocessing system, the one or more instances of sensor data using alossy codec in response to obtaining one or more predictions orclassifications relating to conditions of the respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting. In some of these embodiments, compressing the one or moreinstances of sensor data using the lossy codec includes: normalizing theone or more instances of sensor data into respective pixel values;encoding the respective pixel values into a video frame; and compressinga block of video frames using the lossy codec, wherein the lossy codecis a video codec and the block of video frames includes the video frameand one or more other video frames that include normalized pixel valuesof other instances of the sensor data. In some embodiments, the methodincludes compressing, by the processing system, the one or moreinstances of sensor data using a lossless codec in response to obtaininga prediction or classification relating to a condition of a particularindustrial component or the industrial setting that indicates that thereis likely an issue relating to the particular industrial component orthe industrial setting. In embodiments, the method includes refraining,by the processing system, from compressing the one or more instances ofsensor data in response to obtaining a prediction or classificationrelating to a condition of a particular industrial component or theindustrial setting that indicates that there is likely an issue relatingto the particular industrial component or the industrial setting.

In some embodiments, the edge communication device includes one or morestorage devices that store the plurality of machine-learned models. Insome of these embodiments, the one or more storage devices storeinstances of the sensor data captured by the plurality of sensors of thesensor kit. In some embodiments, the method further includes selectivelystoring, by the processing system, the one or more instances of sensordata in the one or more storage devices based on the respectivepredictions or classifications. In embodiments, the method furtherincludes storing, by the processing system, the one or more instances ofsensor data in the storage device with an expiry such that the one ormore instances of sensor data are purged from the storage device inaccordance with the expiry, wherein the processing system stores the oneor more instances of sensor data in the storage device with the expiryin response to obtaining one or more predictions or classificationsrelating to conditions of respective industrial components of theindustrial setting and the industrial setting that collectively indicatethat there are likely no issues relating to any industrial component ofthe industrial setting and the industrial setting. In some embodiments,the method further includes storing, by the processing system, the oneor more instances of sensor data in the storage device indefinitely inresponse to obtaining a prediction or classification relating to acondition of a particular industrial component or the industrial settingthat indicates that there is likely an issue relating to the particularindustrial component or the industrial setting

In some embodiments, the method further includes capturing, by theplurality of sensors, sensor data; and transmitting, by the plurality ofsensors, the sensor data via a self-configuring sensor kit network. Insome of these embodiments, transmitting the sensor data via theself-configuring sensor kit network includes directly transmitting, byeach sensor of the plurality of sensors, instances of sensor data withthe edge device using a short-range communication protocol, wherein theself-configuring sensor kit network is a star network. In someembodiments, the method further includes initiating, by the processingsystem, configuration of the self-configuring sensor kit network. Inembodiments, the self-configuring sensor kit network is a mesh networkand each sensor of the plurality of sensors includes a communicationdevice. In embodiments, the method further includes: establishing, bythe communication device of each sensor of the plurality of sensors, acommunication channel with at least one other sensor of the plurality ofsensors; receiving, by at least one sensor of the plurality of sensors,instances of sensor data from one or more other sensors of the pluralityof sensors; and routing, by the at least one sensor of the plurality ofsensors, the received instances of the sensor data towards the edgedevice.

In some embodiments, the self-configuring sensor kit network is ahierarchical network and the sensor kit includes one or more collectiondevices. In some of these embodiments, the method further includes:receiving, by at least one collection device of the plurality ofcollection devices, reporting packets from one or more sensors of theplurality of sensors; and routing, by the at least one collection deviceof the plurality of collection devices, the reporting packets to theedge device.

In embodiments, the plurality of sensors includes a first set of sensorsof a first sensor type and a second set of sensors of a second sensortype.

According to some embodiments of the present disclosure, a sensor kitconfigured for monitoring an industrial setting is disclosed. Inembodiments, the sensor kit includes an edge device and a plurality ofsensors that capture sensor data and transmit the sensor data via aself-configuring sensor kit network. The plurality of sensors includesone or more sensors of a first sensor type and one or more sensors of asecond sensor type. At least one sensor of the plurality of sensorsincludes a sensing component that captures sensor measurements andoutputs instances of sensor data; a processing unit that generatesreporting packets based on one or more instances of sensor data andoutputs the reporting packets, wherein each reporting packet includesrouting data and one or more instances of sensor data; and acommunication device configured to receive reporting packets from theprocessing unit and to transmit the reporting packets to the edge devicevia the self-configuring sensor kit network in accordance with a firstcommunication protocol. The edge device includes a first communicationdevice that receives reporting packets from the plurality of sensors viathe self-configuring sensor kit network; and a second communicationdevice that transmits sensor kit packets to a backend system via apublic network. The edge device further includes a processing systemhaving one or more processors that execute computer-executableinstructions that cause the processing system to: receive the reportingpackets from the communication system; generate a block of media contentframes, wherein each media content frame includes a plurality of framevalues, each frame value being indicative of a respective instance ofsensor data; compress the block of media content frames using a mediacodec; generate one or more server kit packets based on the block ofmedia content frames; and transmit the one or more server kit packets tothe backend system via the public network.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In embodiments, the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial setting and/orthe industrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors. In some embodiments, performing one or more edge operationsincludes: generating a feature vector based on one or more instances ofsensor data received from one or more sensors of the plurality ofsensors; inputting the feature vector to the machine-learned model toobtain a prediction or classification relating to a condition of aparticular industrial component of the industrial setting or theindustrial setting and a degree of confidence corresponding to theprediction or classification; and selecting the codec used to compressthe block of media frames based on the condition or prediction. In someembodiments, selecting the codec includes, in response to obtaining oneor more predictions or classifications relating to conditions ofrespective industrial components of the industrial setting and theindustrial setting that collectively indicate that there are likely noissues relating to any industrial component of the industrial settingand the industrial setting, selecting a lossy codec. In some of theseembodiments, selectively encoding the one or more instances of sensordata includes, in response to obtaining a prediction or classificationrelating to a condition of a particular industrial component or theindustrial setting that indicates that there is likely an issue relatingto the particular industrial component or the industrial setting,selecting a lossless codec.

In some embodiments, performing one or more edge operations includes:generating a feature vector based on one or more instances of sensordata received from one or more sensors of the plurality of sensors;inputting the feature vector to the machine-learned model to obtain aprediction or classification relating to a condition of a particularindustrial component of the industrial setting or the industrial settingand a degree of confidence corresponding to the prediction orclassification; and selectively storing the one or more instances ofsensor data in a storage device of the edge device based on theprediction or classification. In some of these embodiments, selectivelystoring the one or more instances of sensor data includes: in responseto obtaining one or more predictions or classifications relating toconditions of respective industrial components of the industrial settingand the industrial setting that collectively indicate that there arelikely no issues relating to any industrial component of the industrialsetting and the industrial setting, storing the one or more instances ofsensor data in the storage device with an expiry, such that the one ormore instances of sensor data are purged from the storage device inaccordance with the expiry. In some embodiments, selectively storing theone or more instances of sensor data includes: in response to obtaininga prediction or classification relating to a condition of a particularindustrial component or the industrial setting that indicates that thereis likely an issue relating to the particular industrial component orthe industrial setting, storing the one or more instances of sensor datain the storage device indefinitely.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In some embodiments, the self-configuring sensor kit network is a meshnetwork such that: the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors, and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitincludes one or more collection devices configured to receive reportingpackets from one or more sensors of the plurality of sensors and routethe reporting packets to the edge device.

In some embodiments, generating the block of media frames includes: foreach instance of sensor data that is to be included in a media frame,normalizing the instance of sensor data into a respective normalizedmedia frame value that is within of range of media frame values that arepermitted by an encoding standard corresponding to the media frame; andembedding each respective normalized media frame value into the mediaframe. In some of these embodiments, wherein each media frame is a videoframe including a plurality of pixels and the respective normalizedmedia frame values are pixel values. In some embodiments, embedding eachrespective normalized media frame value into the media frame includes:determining a pixel of the plurality of pixels corresponding to therespective normalized media frame based on a mapping that mapsrespective sensors of the plurality of sensors to respective pixels ofthe plurality of pixels; and setting a value of the determined pixelequal to the respective normalized media frame value. In embodiments,the codec is an H.264/MPEG-4 codec. In embodiments, the codec is anH.265/MPEG-H codec. In embodiments, the codec is an H.263/MPEG-4 codec.

According to some embodiments of the present disclosure, a method formonitoring an industrial setting using a sensor kit having a pluralityof sensors and an edge device including a processing system isdisclosed. The method includes: receiving, by the processing system,reporting packets from one or more respective sensors of the pluralityof sensors, wherein each reporting packet includes routing data and oneor more instances of sensor data; generating, by the processing system,a block of media content frames, wherein each media content frameincludes a plurality of frame values, each frame value being indicativeof a respective instance of sensor data; compressing, by the processingsystem, the block of media content frames using a media codec to obtaina compressed block; generating, by the processing system, one or moreserver kit packets based on the compressed block; and transmitting, bythe processing system, the one or more server kit packets to a backendsystem via a public network. In some embodiments, the sensor kitincludes a gateway device configured to receive sensor kit packets fromthe edge device via a wired communication link and transmit the sensorkit packets to the backend system via the public network on behalf ofthe edge device. In embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network. In someembodiments, the gateway device includes a cellular chipset that ispre-configured to transmit sensor kit packets to a cellphone tower of apreselected cellular provider.

In embodiments, receiving the reporting packets from the one or morerespective sensors is performed using a first communication device thatreceives reporting packets from the plurality of sensors via aself-configuring sensor kit network and transmitting the sensor kitpackets to the backend system is performed using a second communicationdevice. In some of these embodiments, the second communication device ofthe edge device is a satellite terminal device that is configured totransmit the sensor kit packets to a satellite that routes the sensorkits to the public network. In some embodiments, the method furtherincludes capturing, by the plurality of sensors, sensor data; andtransmitting, by the plurality of sensors, the sensor data to the edgedevice via the self-configuring sensor kit network. In some embodiments,transmitting the sensor data via the self-configuring sensor kit networkincludes directly transmitting, by each sensor of the plurality ofsensors, instances of sensor data with the edge device using ashort-range communication protocol, wherein the self-configuring sensorkit network is a star network. In embodiments, the method furtherincludes initiating, by the processing system, configuration of theself-configuring sensor kit network.

In some embodiments, the self-configuring sensor kit network is a meshnetwork and each sensor of the plurality of sensors includes acommunication device. In some of these embodiments, the method furtherincludes establishing, by the communication device of each sensor of theplurality of sensors, a communication channel with at least one othersensor of the plurality of sensors; receiving, by at least one sensor ofthe plurality of sensors, instances of sensor data from one or moreother sensors of the plurality of sensors; and routing, by the at leastone sensor of the plurality of sensors, the received instances of thesensor data towards the edge device.

In some embodiments, the self-configuring sensor kit network is ahierarchical network and the sensor kit includes one or more collectiondevices. In some of these embodiments, the method further includesreceiving, by at least one collection device of the plurality ofcollection devices, reporting packets from one or more sensors of theplurality of sensors; and routing, by the at least one collection deviceof the plurality of collection devices, the reporting packets to theedge device.

In some embodiments, the method further includes storing, by one or morestorage devices of the edge device, instances of sensor data captured bythe plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial setting and/orthe industrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors. In some of these embodiments, the method further includes:generating, by the processing system, a feature vector based on one ormore instances of sensor data received from one or more sensors of theplurality of sensors; inputting, by the processing system, the featurevector to the machine-learned model to obtain a prediction orclassification relating to a condition of a particular industrialcomponent of the industrial setting or the industrial setting and adegree of confidence corresponding to the prediction or classification;and selecting the media codec used to compress the block of mediacontent frames based on the classification or prediction. In someembodiments, selecting the media codec includes selecting a lossy codecin response to obtaining one or more predictions or classificationsrelating to conditions of respective industrial components of theindustrial setting and the industrial setting that collectively indicatethat there are likely no issues relating to any industrial component ofthe industrial setting and the industrial setting. In embodiments,selecting the media codec includes selecting a lossless codec inresponse to obtaining a prediction or classification relating to acondition of a particular industrial component or the industrial settingthat indicates that there is likely an issue relating to the particularindustrial component or the industrial setting.

In some embodiments, the method further includes: generating, by theprocessing system, a feature vector based on one or more instances ofsensor data received from one or more sensors of the plurality ofsensors; inputting, by the processing system, the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the industrialsetting or the industrial setting and a degree of confidencecorresponding to the prediction or classification; and selectivelystoring, by the processing system, the one or more instances of sensordata in the storage device of the edge device based on the prediction orclassification. In embodiments, selectively storing the one or moreinstances of sensor data in the storage device includes storing the oneor more instances of sensor data in the storage device with an expirysuch that the one or more instances of sensor data are purged from thestorage device in accordance with the expiry, wherein storing the one ormore instances of sensor data in the storage device with an expiry isperformed in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting. In some embodiments, selectively storing the one or moreinstances of sensor data in the storage device includes storing the oneor more instances of sensor data in the storage device indefinitely inresponse to obtaining a prediction or classification relating to acondition of a particular industrial component or the industrial settingthat indicates that there is likely an issue relating to the particularindustrial component or the industrial setting.

In some embodiments, generating the block of media content framesincludes: normalizing, by the processing system, for each instance ofsensor data that is to be included in a media content frame, theinstance of sensor data into a respective normalized media content framevalue that is within of range of media content frame values that arepermitted by an encoding standard corresponding to the media contentframe; and embedding, by the processing system, each respectivenormalized media content frame value into the media content frame. Insome of these embodiments, each media content frame is a video frameincluding a plurality of pixels and the respective normalized mediaframe values are pixel values. In embodiments, embedding each respectivenormalized media content frame value into the media content frameincludes: determining, by the processing system, a pixel of theplurality of pixels corresponding to the respective normalized mediacontent frame based on a mapping that maps respective sensors of theplurality of sensors to respective pixels of the plurality of pixels;and setting a value of the determined pixel equal to the respectivenormalized media content frame value. In some embodiments, the codec isan H.264/MPEG-4 codec. In some embodiments, the codec is an H.265/MPEG-Hcodec. In some embodiments, the codec is an H.263/MPEG-4 codec.

In embodiments, the plurality of sensors includes a first set of sensorsof a first sensor type and a second set of sensors of a second sensortype.

According to some embodiments of the present disclosure, a system isdisclosed. The system includes a backend system and a sensor kitconfigured to monitor an industrial setting, the sensor kit. The sensorkit includes a plurality of sensors that capture sensor data andtransmit the sensor data via a self-configuring sensor kit network,wherein the plurality of sensors includes one or more sensors of a firstsensor type and one or more sensors of a second sensor type, wherein atleast one sensor of the plurality of sensors includes: a sensingcomponent that captures sensor measurements and outputs instances ofsensor data; a processing unit that generates reporting packets based onone or more instances of sensor data and outputs the reporting packets,wherein each reporting packet includes routing data and one or moreinstances of sensor data; and a communication device configured toreceive reporting packets from the processing unit and to transmit thereporting packets to the edge device via the self-configuring sensor kitnetwork in accordance with a first communication protocol. The edgedevice includes a communication system having: a first communicationdevice that receives reporting packets from the plurality of sensors viathe self-configuring sensor kit network; and a second communicationdevice that transmits sensor kit packets to a backend system via apublic network. The edge device includes a processing system having oneor more processors that execute computer-executable instructions thatcause the processing system to: receive the reporting packets from thecommunication system; perform one or more edge operations on theinstances of sensor data in the reporting packets; generate the sensorkit packets based on the instances of sensor data, wherein each sensorkit packet includes at least one instance of sensor data; and output thesensor kits packets to the communication system, wherein thecommunication system transmits the reporting packets to the backendsystem via the public network. The backend system includes a backendstorage system that stores a sensor kit data store that stores sensordata received from one or more respective sensor kits, including thesensor kit; and a backend processing system having one or moreprocessors that execute computer-executable instructions that cause thebackend processing system to: receive the sensor kit packets from thesensor kit; determine sensor data collected by the sensor kit based onthe sensor kit packets; perform one or more backend operations on thesensor data collected by the sensor kit; and store the sensor datacollected by the sensor kit in the sensor kit data store.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In embodiments, the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial setting and/orthe industrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors. In some of these embodiments, performing one or more edgeoperations includes: generating a feature vector based on one or moreinstances of sensor data received from one or more sensors of theplurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the industrialsetting or the industrial setting and a degree of confidencecorresponding to the prediction or classification; and selectivelyencoding the one or more instances of sensor data prior to transmissionto the backend system based on the condition or prediction. In someembodiments, selectively encoding the one or more instances of sensordata includes: in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting, compressing the one or more instances of sensor data using alossy codec. In some embodiments, compressing the one or more instancesof sensor data using the lossy codec includes: normalizing the one ormore instances of sensor data into respective pixel values; encoding therespective pixel values into a video frame; and compressing a block ofvideo frames using the lossy codec to obtain a compressed block offrames, wherein the lossy codec is a video codec and the block of videoframes includes the video frame and one or more other video frames thatinclude normalized pixel values of other instances of sensor data. Inembodiments, the backend system receives the compressed block of framesin one or more sensor kit packets and determines the sensor datacollected by the sensor kit by decompressing the compressed block offrames using the lossy codec. In some embodiments, selectively encodingthe one or more instances of sensor data includes, in response toobtaining a prediction or classification relating to a condition of aparticular industrial component or the industrial setting that indicatesthat there is likely an issue relating to the particular industrialcomponent or the industrial setting, compressing the one or moreinstances of sensor data using a lossless codec. In embodiments,selectively encoding the one or more instances of sensor data includes,in response to obtaining a prediction or classification relating to acondition of a particular industrial component or the industrial settingthat indicates that there is likely an issue relating to the particularindustrial component or the industrial setting, refraining fromcompressing the one or more instances of sensor data. In embodiments,selectively encoding the one or more instances of sensor data includesselecting a stream of sensor data instances for uncompressedtransmission. In embodiments, performing one or more edge operationsincludes: generating a feature vector based on one or more instances ofsensor data received from one or more sensors of the plurality ofsensors; inputting the feature vector to the machine-learned model toobtain a prediction or classification relating to a condition of aparticular industrial component of the industrial setting or theindustrial setting and a degree of confidence corresponding to theprediction or classification; and selectively storing the one or moreinstances of sensor data in a storage device of the edge device based onthe prediction or classification. In some of these embodiments,selectively storing the one or more instances of sensor data includes,in response to obtaining one or more predictions or classificationsrelating to conditions of respective industrial components of theindustrial setting and the industrial setting that collectively indicatethat there are likely no issues relating to any industrial component ofthe industrial setting and the industrial setting, storing the one ormore instances of sensor data in the storage device with an expiry, suchthat the one or more instances of sensor data are purged from thestorage device in accordance with the expiry. In some embodiments,selectively storing the one or more instances of sensor data includes,in response to obtaining a prediction or classification relating to acondition of a particular industrial component or the industrial settingthat indicates that there is likely an issue relating to the particularindustrial component or the industrial setting, storing the one or moreinstances of sensor data in the storage device indefinitely.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In some embodiments, the self-configuring sensor kit network is a meshnetwork such that: the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors, and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitincludes one or more collection devices configured to receive reportingpackets from one or more sensors of the plurality of sensors and routethe reporting packets to the edge device.

In embodiments, the backend operations include performing one or moreanalytics tasks using the sensor data; performing one or more artificialintelligence tasks using the sensor data; issuing a notification to ahuman user associated with the industrial setting based on the sensordata; and/or controlling at least one component of the industrialsetting based on the sensor data.

According to some embodiments of the present disclosure, a method formonitoring an industrial setting using a sensor kit in communicationwith a backend system, the sensor kit including a plurality of sensorsand an edge device is disclosed. The method includes: receiving, by anedge processing system of the edge device, reporting packets from one ormore respective sensors of the plurality of sensors, wherein eachreporting packet includes routing data and one or more instances ofsensor data; performing, by the edge processing system, one or more edgeoperations on the instances of sensor data in the reporting packets;generating, by the edge processing system, a plurality of sensor kitpackets based on the instances of sensor data, wherein each sensor kitpacket includes at least one instance of sensor data; transmitting, bythe edge processing system, the sensor kit packets to the backend systemvia a public network; receiving, by a backend processing system of thebackend system, the sensor kit packets from the sensor kit via thepublic network; determining, by the backend processing system, thesensor data collected by the sensor kit based on the sensor kit packets;performing, by the backend processing system, one or more backendoperations on the sensor data collected by the sensor kit; and storing,by the backend processing system, the sensor data collected by thesensor kit in a sensor kit data store residing in a backend storagesystem of the backend system. In some embodiments, the sensor kitfurther includes a gateway device, wherein the gateway device isconfigured to receive sensor kit packets from the edge device via awired communication link and transmit the sensor kit packets to thebackend system via the public network on behalf of the edge device. Insome embodiments, the gateway device includes a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network. Inembodiments, the gateway device includes a cellular chipset that ispre-configured to transmit sensor kit packets to a cellphone tower of apreselected cellular provider.

In embodiments, receiving the reporting packets from the one or morerespective sensors is performed using a first communication device ofthe edge device that receives reporting packets from the plurality ofsensors via a self-configuring sensor kit network and transmitting thesensor kit packets to the backend system is performed using a secondcommunication device of the edge device. In some of these embodiments,the second communication device of the edge device is a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network. Inembodiments, the method further includes capturing, by the plurality ofsensors, sensor data; and transmitting, by the plurality of sensors, thesensor data to the edge device via the self-configuring sensor kitnetwork. In some embodiments, transmitting the sensor data via theself-configuring sensor kit network includes directly transmitting, byeach sensor of the plurality of sensors, instances of sensor data withthe edge device using a short-range communication protocol, wherein theself-configuring sensor kit network is a star network. In embodiments,the method further includes initiating, by the edge processing system,configuration of the self-configuring sensor kit network. In someembodiments, the self-configuring sensor kit network is a mesh networkand each sensor of the plurality of sensors includes a communicationdevice. In some embodiments, the method further includes: establishing,by the communication device of each sensor of the plurality of sensors,a communication channel with at least one other sensor of the pluralityof sensors; receiving, by at least one sensor of the plurality ofsensors, instances of sensor data from one or more other sensors of theplurality of sensors; and routing, by the at least one sensor of theplurality of sensors, the received instances of the sensor data towardsthe edge device.

In some embodiments, the self-configuring sensor kit network is ahierarchical network and the sensor kit includes one or more collectiondevices. In some of these embodiments, the method further includes:receiving, by at least one collection device of the plurality ofcollection devices, reporting packets from one or more sensors of theplurality of sensors; and routing, by the at least one collection deviceof the plurality of collection devices, the reporting packets to theedge device.

In embodiments, the method further includes storing, by one or morestorage devices of the edge device, instances of sensor data captured bythe plurality of sensors of the sensor kit.

In some embodiments, the edge device further includes one or morestorage devices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial setting and/orthe industrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors. In some of these embodiments, performing one or more edgeoperations includes: generating, by the edge processing system, afeature vector based on one or more instances of sensor data receivedfrom one or more sensors of the plurality of sensors; inputting, by theedge processing system, the feature vector to the machine-learned modelto obtain a prediction or classification relating to a condition of aparticular industrial component of the industrial setting or theindustrial setting and a degree of confidence corresponding to theprediction or classification; and selectively encoding, by the edgeprocessing system, the one or more instances of sensor data prior totransmission to the backend system based on the prediction orclassification. In some embodiments, selectively encoding the one ormore instances of sensor data includes compressing, by the edgeprocessing system, the one or more instances of sensor data using alossy codec in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting. In some embodiments, compressing the one or more instances ofsensor data using a lossy codec includes: normalizing, by the edgeprocessing system, the one or more instances of sensor data intorespective pixel values; encoding, by the edge processing system, therespective pixel values into a media content frame; and compressing, bythe edge processing system, a block of media content frames using thelossy codec to obtain a compressed block, wherein the lossy codec is avideo codec and the compressed block includes the media content frameand one or more other media content frames that include normalized pixelvalues of other instances of sensor data. In embodiments, the backendsystem receives the compressed block in one or more sensor kit packetsand determines the sensor data collected by the sensor kit bydecompressing the compressed block using the lossy codec.

In some embodiments, selectively encoding the one or more instances ofsensor data includes compressing, by the edge processing system, the oneor more instances of sensor data using a lossless codec in response toobtaining a prediction or classification relating to a condition of aparticular industrial component or the industrial setting that indicatesthat there is likely an issue relating to the particular industrialcomponent or the industrial setting. In embodiments, selectivelyencoding the one or more instances of sensor data includes refraining,by the edge processing system, from compressing the one or moreinstances of sensor data in response to obtaining a prediction orclassification relating to a condition of a particular industrialcomponent or the industrial setting that indicates that there is likelyan issue relating to the particular industrial component or theindustrial setting. In some embodiments, selectively encoding the one ormore instances of sensor data includes selecting, by the edge processingsystem, a stream of sensor data instances for uncompressed transmission.

In some embodiments, performing one or more edge operations includes:generating, by the edge processing system, a feature vector based on oneor more instances of sensor data received from one or more sensors ofthe plurality of sensors; inputting, by the edge processing system, thefeature vector to the machine-learned model to obtain a prediction orclassification relating to a condition of a particular industrialcomponent of the industrial setting or the industrial setting and adegree of confidence corresponding to the prediction or classification;and selectively storing, by the edge processing system, the one or moreinstances of sensor data in a storage device of the one or more storagedevices based on the prediction or classification. In some embodiments,selectively storing the one or more instances of sensor data includesstoring, by the edge processing system, the one or more instances ofsensor data in the storage device with an expiry in response toobtaining one or more predictions or classifications relating toconditions of respective industrial components of the industrial settingand the industrial setting that collectively indicate that there arelikely no issues relating to any industrial component of the industrialsetting and the industrial setting, wherein storing the one or moreinstances of sensor data in the storage device with an expiry isperformed such that the one or more instances of sensor data are purgedfrom the storage device in accordance with the expiry. In someembodiments, selectively storing the one or more instances of sensordata includes storing, by the edge processing system, the one or moreinstances of sensor data in the storage device indefinitely in responseto obtaining a prediction or classification relating to a condition of aparticular industrial component or the industrial setting that indicatesthat there is likely an issue relating to the particular industrialcomponent or the industrial setting.

In some embodiments, the plurality of sensors includes a first set ofsensors of a first sensor type and a second set of sensors of a secondsensor type.

According to some embodiments of the present disclosure, a sensor kitconfigured to monitor an indoor agricultural facility is disclosed. Thesensor kit includes an edge device and a plurality of sensors thatcapture sensor data and transmit the sensor data via a self-configuringsensor kit network, wherein the plurality of sensors includes one ormore sensors of a first sensor type and one or more sensors of a secondsensor type. At least one sensor of the plurality of sensors includes: asensing component that captures sensor measurements and outputsinstances of sensor data; a processing unit that generates reportingpackets based on one or more instances of sensor data and outputs thereporting packets, wherein each reporting packet includes routing dataand one or more instances of sensor data; and a communication deviceconfigured to receive reporting packets from the processing unit and totransmit the reporting packets to the edge device via theself-configuring sensor kit network in accordance with a firstcommunication protocol. The plurality of sensors includes two or moresensor types selected from the group including: light sensors, humiditysensors, temperature sensors, carbon dioxide sensors, fan speed sensors,weight sensors, and camera sensors. The edge device includes acommunication system having a first communication device that receivesreporting packets from the plurality of sensors via the self-configuringsensor kit network and a second communication device that transmitssensor kit packets to a backend system via a public network. The edgedevice also includes a processing system having one or more processorsthat execute computer-executable instructions that cause the processingsystem to: receive the reporting packets from the communication system,perform one or more edge operations on the instances of sensor data inthe reporting packets; generate the sensor kit packets based on theinstances of sensor data, wherein each sensor kit packet includes atleast one instance of sensor data; and output the sensor kits packets tothe communication system, wherein the communication system transmits thereporting packets to the backend system via the public network.

In embodiments, the sensor kit includes an edge device and a pluralityof sensors that capture sensor data and transmit the sensor data via aself-configuring sensor kit network. The plurality of sensors includesone or more sensors of a first sensor type and one or more sensors of asecond sensor type. At least one sensor of the plurality of sensorsincludes a sensing component that captures sensor measurements andoutputs instances of sensor data; a processing unit that generatesreporting packets based on one or more instances of sensor data andoutputs the reporting packets, wherein each reporting packet includesrouting data and one or more instances of sensor data; and acommunication device configured to receive reporting packets from theprocessing unit and to transmit the reporting packets to the edge devicevia the self-configuring sensor kit network in accordance with a firstcommunication protocol.

In embodiments, the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of a component of the indoor agricultural setting and/or theindoor agricultural setting based on a set of features that are derivedfrom instances of sensor data captured by one or more of the pluralityof sensors. In some of these embodiments, performing one or more edgeoperations includes: generating a feature vector based on one or moreinstances of sensor data received from one or more sensors of theplurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular component of the indoor agriculturalsetting or the indoor agricultural setting and a degree of confidencecorresponding to the prediction or classification; and selectivelyencoding the one or more instances of sensor data prior to transmissionto the backend system based on the condition or prediction. In someembodiments, selectively encoding the one or more instances of sensordata includes compressing the one or more instances of sensor data usinga lossy codec in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the indoor agricultural setting and the indooragricultural setting that collectively indicate that there are likely noissues relating to any component of the indoor agricultural setting andthe indoor agricultural setting. In some embodiments, compressing theone or more instances of sensor data using the lossy codec includes:normalizing the one or more instances of sensor data into respectivepixel values; encoding the respective pixel values into a video frame;and compressing a block of video frames using the lossy codec, whereinthe lossy codec is a video codec and the block of video frames includesthe video frame and one or more other video frames that includenormalized pixel values of other instances of sensor data. In someembodiments, selectively encoding the one or more instances of sensordata includes: compressing the one or more instances of sensor datausing a lossless codec in response to obtaining a prediction orclassification relating to a condition of a particular industrialcomponent or the industrial setting that indicates that there is likelyan issue relating to the particular industrial component or theindustrial setting. In embodiments, selectively encoding the one or moreinstances of sensor data includes refraining from compressing the one ormore instances of sensor data in response to obtaining a prediction orclassification relating to a condition of a particular component or theindoor agricultural setting that indicates that there is likely an issuerelating to the particular component or the indoor agricultural setting.In embodiments, performing one or more edge operations includes:generating a feature vector based on one or more instances of sensordata received from one or more sensors of the plurality of sensors;inputting the feature vector to the machine-learned model to obtain aprediction or classification relating to a condition of a particularcomponent of the indoor agricultural setting or the indoor agriculturalsetting and a degree of confidence corresponding to the prediction orclassification; and selectively storing the one or more instances ofsensor data in a storage device of the edge device based on theprediction or classification. In some of these embodiments, selectivelystoring the one or more instances of sensor data includes storing theone or more instances of sensor data in the storage device with anexpiry in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the indoor agricultural setting and the indooragricultural setting that collectively indicate that there are likely noissues relating to any component of the indoor agricultural setting andthe indoor agricultural setting, such that the one or more instances ofsensor data are purged from the storage device in accordance with theexpiry. In some embodiments, selectively storing the one or moreinstances of sensor data includes storing the one or more instances ofsensor data in the storage device indefinitely in response to obtaininga prediction or classification relating to a condition of a particularindustrial component or the industrial setting that indicates that thereis likely an issue relating to the particular component or the indooragricultural setting.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is a meshnetwork such that: the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors; and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitfurther includes one or more collection devices configured to receivereporting packets from one or more sensors of the plurality of sensorsand route the reporting packets to the edge device. In embodiments, eachcollection device is installed in a different respective room of theindoor agricultural setting and collects sensor data from sensors of theplurality sensors that are deployed in the respective room.

According to some embodiments of the present disclosure, a sensor kitconfigured to monitor an indoor agricultural setting is disclosed. Thesensor kit includes an edge device and a plurality of sensors thatcapture sensor data and transmit the sensor data via a self-configuringsensor kit network, wherein the plurality of sensors includes one ormore sensors of a first sensor type and one or more sensors of a secondsensor type. At least one sensor of the plurality of sensors includes: asensing component that captures sensor measurements and outputsinstances of sensor data; a processing unit that generates reportingpackets based on one or more instances of sensor data and outputs thereporting packets, wherein each reporting packet includes routing dataand one or more instances of sensor data; and a communication deviceconfigured to receive reporting packets from the processing unit and totransmit the reporting packets to the edge device via theself-configuring sensor kit network in accordance with a firstcommunication protocol. The plurality of sensors includes two or moresensor types selected from the group including: infrared sensors, groundpenetrating sensors, light sensors, humidity sensors, temperaturesensors, chemical sensors, fan speed sensors, rotational speed sensors,weight sensors, and camera sensors. The edge device includes acommunication system having a first communication device that receivesreporting packets from the plurality of sensors via the self-configuringsensor kit network and a second communication device that transmitssensor kit packets to a backend system via a public network. The edgedevice further includes a processing system having one or moreprocessors that execute computer-executable instructions that cause theprocessing system to: receive the reporting packets from thecommunication system; perform one or more edge operations on theinstances of sensor data in the reporting packets; generate the sensorkit packets based on the instances of sensor data, wherein each sensorkit packet includes at least one instance of sensor data; and output thesensor kits packets to the communication system, wherein thecommunication system transmits the reporting packets to the backendsystem via the public network.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In embodiments, the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of a component of the indoor agricultural setting and/or theindoor agricultural setting based on a set of features that are derivedfrom instances of sensor data captured by one or more of the pluralityof sensors. In some embodiments, performing one or more edge operationsincludes: generating a feature vector based on one or more instances ofsensor data received from one or more sensors of the plurality ofsensors; inputting the feature vector to the machine-learned model toobtain a prediction or classification relating to a condition of aparticular component of the indoor agricultural setting or the indooragricultural and a degree of confidence corresponding to the predictionor classification; and selectively encoding the one or more instances ofsensor data prior to transmission to the backend system based on thecondition or prediction.

In embodiments, selectively encoding the one or more instances of sensordata includes compressing the one or more instances of sensor data usinga lossy codec in response to obtaining one or more predictions orclassifications relating to conditions of respective components of theindoor agricultural setting and the indoor agricultural setting thatcollectively indicate that there are likely no issues relating to anycomponent of the indoor agricultural setting and the indoor agriculturalsetting. In embodiments, compressing the one or more instances of sensordata using the lossy codec includes: normalizing the one or moreinstances of sensor data into respective pixel values; encoding therespective pixel values into a video frame; and compressing a block ofvideo frames using the lossy codec, wherein the lossy codec is a videocodec and the block of video frames includes the video frame and one ormore other video frames that include normalized pixel values of otherinstances of sensor data. In embodiments, selectively encoding the oneor more instances of sensor data includes compressing the one or moreinstances of sensor data using a lossless codec in response to obtaininga prediction or classification relating to a condition of a particularcomponent or the indoor agricultural setting that indicates that thereis likely an issue relating to the particular component or the indooragricultural setting. In embodiments, selectively encoding the one ormore instances of sensor data includes refraining from compressing theone or more instances of sensor data in response to obtaining aprediction or classification relating to a condition of a particularcomponent or the indoor agricultural setting that indicates that thereis likely an issue relating to the particular component or the indooragricultural setting.

In some embodiments, performing one or more edge operations includes:generating a feature vector based on one or more instances of sensordata received from one or more sensors of the plurality of sensors;inputting the feature vector to the machine-learned model to obtain aprediction or classification relating to a condition of a particularcomponent of the indoor agricultural setting or the indoor agriculturalsetting and a degree of confidence corresponding to the prediction orclassification; and selectively storing the one or more instances ofsensor data in a storage device of the edge device based on theprediction or classification. In embodiments, selectively storing theone or more instances of sensor data includes storing the one or moreinstances of sensor data in the storage device with an expiry inresponse to obtaining one or more predictions or classificationsrelating to conditions of respective components of the indooragricultural setting and the indoor agricultural setting thatcollectively indicate that there are likely no issues relating to anycomponent of the indoor agricultural setting and the indoor agriculturalsetting, such that the one or more instances of sensor data are purgedfrom the storage device in accordance with the expiry. In embodiments,selectively storing the one or more instances of sensor data includesstoring the one or more instances of sensor data in the storage deviceindefinitely in response to obtaining a prediction or classificationrelating to a condition of a particular component or the indooragricultural setting that indicates that there is likely an issuerelating to the particular component or the indoor agricultural setting.

In some embodiments, the plurality of sensors includes a first set ofsensors of a first sensor type and a second set of sensors of a secondsensor type selected from the group including: light sensors, humiditysensors, temperature sensors, carbon dioxide sensors, fan speed sensors,weight sensors, and camera sensors.

According to some embodiments of the present disclosure, a sensor kitconfigured to monitor a pipeline setting is disclosed. The sensor kitincludes an edge device and a plurality of sensors that capture sensordata and transmit the sensor data via a self-configuring sensor kitnetwork. The plurality of sensors includes one or more sensors of afirst sensor type and one or more sensors of a second sensor type. Atleast one sensor of the plurality of sensors includes: a sensingcomponent that captures sensor measurements and outputs instances ofsensor data; a processing unit that generates reporting packets based onone or more instances of sensor data and outputs the reporting packets,wherein each reporting packet includes routing data and one or moreinstances of sensor data; and a communication device configured toreceive reporting packets from the processing unit and to transmit thereporting packets to the edge device via the self-configuring sensor kitnetwork in accordance with a first communication protocol. The pluralityof sensors includes two or more sensor types selected from the groupincluding: infrared sensors, metal penetrating sensors, concretepenetrating sensors, light sensors, strain sensors, rust sensors,biological sensors, humidity sensors, temperature sensors, chemicalsensors, valve integrity sensors, vibration sensors, flow sensors,cavitation sensors, pressure sensors, weight sensors, and camerasensors. The edge device includes a communication system having: a firstcommunication device that receives reporting packets from the pluralityof sensors via the self-configuring sensor kit network and a secondcommunication device that transmits sensor kit packets to a backendsystem via a public network. The edge device further includes aprocessing system having one or more processors that executecomputer-executable instructions that cause the processing system to:receive the reporting packets from the communication system; perform oneor more edge operations on the instances of sensor data in the reportingpackets; generate the sensor kit packets based on the instances ofsensor data, wherein each sensor kit packet includes at least oneinstance of sensor data; and output the sensor kits packets to thecommunication system, wherein the communication system transmits thereporting packets to the backend system via the public network.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In embodiments, the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of a pipeline component of the pipeline setting and/or thepipeline setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors. In some of these embodiments, performing one or more edgeoperations includes: generating a feature vector based on one or moreinstances of sensor data received from one or more sensors of theplurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular pipeline component of the pipelinesetting or the pipeline setting and a degree of confidence correspondingto the prediction or classification; and selectively encoding the one ormore instances of sensor data prior to transmission to the backendsystem based on the condition or prediction. In embodiments, selectivelyencoding the one or more instances of sensor data includes compressingthe one or more instances of sensor data using a lossy codec in responseto obtaining one or more predictions or classifications relating toconditions of respective pipeline components of the pipeline setting andthe pipeline setting that collectively indicate that there are likely noissues relating to any pipeline component of the pipeline setting andthe pipeline setting. In embodiments, compressing the one or moreinstances of sensor data using the lossy codec includes: normalizing theone or more instances of sensor data into respective pixel values;encoding the respective pixel values into a video frame; and compressinga block of video frames using the lossy codec, wherein the lossy codecis a video codec and the block of video frames includes the video frameand one or more other video frames that include normalized pixel valuesof other instances of sensor data. In embodiments, selectively encodingthe one or more instances of sensor data includes compressing the one ormore instances of sensor data using a lossless codec in response toobtaining a prediction or classification relating to a condition of aparticular pipeline component or the pipeline setting that indicatesthat there is likely an issue relating to the particular pipelinecomponent or the pipeline setting. In embodiments, selectively encodingthe one or more instances of sensor data includes refraining fromcompressing the one or more instances of sensor data in response toobtaining a prediction or classification relating to a condition of aparticular pipeline component or the pipeline setting that indicatesthat there is likely an issue relating to the particular pipelinecomponent or the pipeline setting. In embodiments, performing one ormore edge operations includes generating a feature vector based on oneor more instances of sensor data received from one or more sensors ofthe plurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular pipeline component of the pipelinesetting or the pipeline setting and a degree of confidence correspondingto the prediction or classification; and selectively storing the one ormore instances of sensor data in a storage device of the edge devicebased on the prediction or classification. In embodiments, selectivelystoring the one or more instances of sensor data includes storing theone or more instances of sensor data in the storage device with anexpiry in response to obtaining one or more predictions orclassifications relating to conditions of respective pipeline componentsof the pipeline setting and the pipeline setting that collectivelyindicate that there are likely no issues relating to any pipelinecomponent of the pipeline setting and the pipeline setting, such thatthe one or more instances of sensor data are purged from the storagedevice in accordance with the expiry. In embodiments, selectivelystoring the one or more instances of sensor data includes storing theone or more instances of sensor data in the storage device indefinitelyin response to obtaining a prediction or classification relating to acondition of a particular pipeline component or the pipeline settingthat indicates that there is likely an issue relating to the particularpipeline component or the pipeline setting.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is a meshnetwork such that: the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors; and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitfurther includes one or more collection devices configured to receivereporting packets from one or more sensors of the plurality of sensorsand route the reporting packets to the edge device. In embodiments, eachcollection device is installed in a different respective section of thepipeline setting and collects sensor data from sensors of the pluralitysensors that are deployed in the respective room.

According to some embodiments of the present disclosure, a method ofmonitoring a pipeline setting using a sensor kit including an edgedevice and a plurality of sensors is disclosed. The method includes:receiving, by an edge processing system of the edge device, reportingpackets from a plurality of sensors via a self-configuring sensor kitnetwork, each reporting packet containing routing data and one or moreinstances of sensor data captured by a respective sensor of theplurality of sensors, wherein the plurality of sensors includes two ormore sensor types selected from the group including: light sensors,humidity sensors, temperature sensors, carbon dioxide sensors, fan speedsensors, weight sensors, and camera sensors; performing, by the edgeprocessing system, one or more edge operations on the instances ofsensor data in the reporting packets; generating, by the edge processingsystem, one or more edge operations on the instances of sensor data inthe reporting packets; and transmitting, by the edge processing system,the sensor kit packets to an edge communication system of the edgedevice, wherein the edge communication system transmits the reportingpackets to a backend system via a public network. In some embodiments,the sensor kit further includes a gateway device, wherein the gatewaydevice is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In embodiments, the gateway device includes a satellite terminal devicethat is configured to transmit the sensor kit packets to a satellitethat routes the sensor kits to the public network. In some embodiments,the gateway device includes a cellular chipset that is pre-configured totransmit sensor kit packets to a cellphone tower of a preselectedcellular provider. In embodiments, receiving the reporting packets fromthe one or more respective sensors is performed using a firstcommunication device of the edge device that receives reporting packetsfrom the plurality of sensors via a self-configuring sensor kit networkand transmitting the sensor kit packets to the backend system isperformed using a second communication device of the edge device. Insome embodiments, the second communication device of the edge device isa satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In some embodiments, the method further includes capturing, by theplurality of sensors, sensor data; and transmitting, by the plurality ofsensors, the sensor data to the edge device via the self-configuringsensor kit network. In some of these embodiments, transmitting thesensor data via the self-configuring sensor kit network includesdirectly transmitting, by each sensor of the plurality of sensors,instances of sensor data with the edge device using a short-rangecommunication protocol, wherein the self-configuring sensor kit networkis a star network. In some embodiments, the method further includesinitiating, by the edge processing system, configuration of theself-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is a meshnetwork and each sensor of the plurality of sensors includes acommunication device. In some of these embodiments, the method furtherincludes: establishing, by the communication device of each sensor ofthe plurality of sensors, a communication channel with at least oneother sensor of the plurality of sensors; receiving, by at least onesensor of the plurality of sensors, instances of sensor data from one ormore other sensors of the plurality of sensors; and routing, by the atleast one sensor of the plurality of sensors, the received instances ofthe sensor data towards the edge device.

In some embodiments, the self-configuring sensor kit network is ahierarchical network and the sensor kit includes one or more collectiondevices. In some of these embodiments, the method further includes:receiving, by at least one collection device of the plurality ofcollection devices, reporting packets from one or more sensors of theplurality of sensors; and routing, by the at least one collection deviceof the plurality of collection devices, the reporting packets to theedge device. In some embodiments, each collection device is installed ina different respective section of the pipeline setting and collectssensor data from sensors of the plurality sensors that are deployed inthe respective room.

In some embodiments, the method further includes storing, by one or morestorage devices of the edge device, instances of sensor data captured bythe plurality of sensors of the sensor kit. In embodiments, the edgedevice further includes one or more storage devices that store a modeldata store that stores one or more machine-learned models that are eachtrained to predict or classify a condition of a component of theagricultural setting and/or the agricultural setting based on a set offeatures that are derived from instances of sensor data captured by oneor more of the plurality of sensors.

In some embodiments, performing one or more edge operations includes:generating, by the edge processing system, a feature vector based on oneor more instances of sensor data received from one or more sensors ofthe plurality of sensors; inputting, by the edge processing system, thefeature vector to the machine-learned model to obtain a prediction orclassification relating to a condition of a particular component of theagricultural setting or the agricultural setting and a degree ofconfidence corresponding to the prediction or classification; andselectively encoding, by the edge processing system, the one or moreinstances of sensor data prior to transmission to the backend systembased on the prediction or classification. In some of these embodiments,selectively encoding the one or more instances of sensor data includescompressing, by the edge processing system, the one or more instances ofsensor data using a lossy codec in response to obtaining one or morepredictions or classifications relating to conditions of respectivecomponents of the agricultural setting and the agricultural setting thatcollectively indicate that there are likely no issues relating to anycomponent of the agricultural setting and the agricultural setting. Insome embodiments, compressing the one or more instances of sensor datausing a lossy codec includes: normalizing, by the edge processingsystem, the one or more instances of sensor data into respective pixelvalues; encoding, by the edge processing system, the respective pixelvalues into a media content frame; and compressing, by the edgeprocessing system, a block of media content frames using the lossy codecto obtain a compressed block, wherein the lossy codec is a video codecand the compressed block includes the media content frame and one ormore other media content frames that include normalized pixel values ofother instances of sensor data. In some embodiments, the backend systemreceives the compressed block in one or more sensor kit packets anddetermines the sensor data collected by the sensor kit by decompressingthe compressed block using the lossy codec.

In some embodiments, selectively encoding the one or more instances ofsensor data includes compressing, by the edge processing system, the oneor more instances of sensor data using a lossless codec in response toobtaining a prediction or classification relating to a condition of aparticular component or the agricultural setting that indicates thatthere is likely an issue relating to the particular component or theagricultural setting. In embodiments, encoding the one or more instancesof sensor data includes refraining, by the edge processing system, fromcompressing the one or more instances of sensor data in response toobtaining a prediction or classification relating to a condition of aparticular component or the agricultural setting that indicates thatthere is likely an issue relating to the particular component or theagricultural setting. In some embodiments, selectively encoding the oneor more instances of sensor data includes selecting, by the edgeprocessing system, a stream of sensor data instances for uncompressedtransmission.

In some embodiments, performing one or more edge operations includes:generating, by the edge processing system, a feature vector based on oneor more instances of sensor data received from one or more sensors ofthe plurality of sensors; inputting, by the edge processing system, thefeature vector to the machine-learned model to obtain a prediction orclassification relating to a condition of a particular component of theagricultural setting or the agricultural setting and a degree ofconfidence corresponding to the prediction or classification; andselectively storing, by the edge processing system, the one or moreinstances of sensor data in a storage device of the one or more storagedevices based on the prediction or classification. In some of theseembodiments, selectively storing the one or more instances of sensordata includes storing, by the edge processing system, the one or moreinstances of sensor data in the storage device with an expiry inresponse to obtaining one or more predictions or classificationsrelating to conditions of respective components of the agriculturalsetting and the agricultural setting that collectively indicate thatthere are likely no issues relating to any component of the agriculturalsetting and the agricultural setting, wherein storing the one or moreinstances of sensor data in the storage device with an expiry isperformed such that the one or more instances of sensor data are purgedfrom the storage device in accordance with the expiry. In someembodiments, selectively storing the one or more instances of sensordata includes storing, by the edge processing system, the one or moreinstances of sensor data in the storage device indefinitely in responseto obtaining a prediction or classification relating to a condition of aparticular component or the agricultural setting that indicates thatthere is likely an issue relating to the particular component or theagricultural setting. In some embodiments, the plurality of sensorsincludes a first set of sensors of a first sensor type and a second setof sensors of a second sensor type selected from the group including:light sensors, humidity sensors, temperature sensors, carbon dioxidesensors, fan speed sensors, weight sensors, and camera sensors.

According to some embodiments of the present disclosure, a sensor kitconfigured to monitor an industrial manufacturing setting is disclosed.The sensor kit includes an edge device and a plurality of sensors thatcapture sensor data and transmit the sensor data via a self-configuringsensor kit network, wherein the plurality of sensors includes one ormore sensors of a first sensor type and one or more sensors of a secondsensor type. At least one sensor of the plurality of sensors includes asensing component that captures sensor measurements and outputsinstances of sensor data; a processing unit that generates reportingpackets based on one or more instances of sensor data and outputs thereporting packets, wherein each reporting packet includes routing dataand one or more instances of sensor data; and a communication deviceconfigured to receive reporting packets from the processing unit and totransmit the reporting packets to the edge device via theself-configuring sensor kit network in accordance with a firstcommunication protocol. The plurality of sensors includes two or moresensor types selected from the group including: metal penetratingsensors, concrete penetrating sensors, vibration sensors, light sensors,strain sensors, rust sensors, biological sensors, temperature sensors,chemical sensors, valve integrity sensors, rotational speed sensors,vibration sensors, flow sensors, cavitation sensors, pressure sensors,weight sensors, and camera sensors. The edge device includes acommunication system having a first communication device that receivesreporting packets from the plurality of sensors via the self-configuringsensor kit network; and a second communication device that transmitssensor kit packets to a backend system via a public network. The edgedevice further includes a processing system having one or moreprocessors that execute computer-executable instructions that cause theprocessing system to: receive the reporting packets from thecommunication system; perform one or more edge operations on theinstances of sensor data in the reporting packets; generate the sensorkit packets based on the instances of sensor data, wherein each sensorkit packet includes at least one instance of sensor data; and output thesensor kits packets to the communication system, wherein thecommunication system transmits the reporting packets to the backendsystem via the public network.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In embodiments, the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial manufacturingsetting and/or the industrial manufacturing setting based on a set offeatures that are derived from instances of sensor data captured by oneor more of the plurality of sensors. In some embodiments, performing oneor more edge operations includes: generating a feature vector based onone or more instances of sensor data received from one or more sensorsof the plurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the industrialmanufacturing setting or the industrial manufacturing setting and adegree of confidence corresponding to the prediction or classification;and selectively encoding the one or more instances of sensor data priorto transmission to the backend system based on the condition orprediction. In some of these embodiments, selectively encoding the oneor more instances of sensor data includes compressing the one or moreinstances of sensor data using a lossy codec in response to obtainingone or more predictions or classifications relating to conditions ofrespective industrial components of the industrial manufacturing settingand the industrial manufacturing setting that collectively indicate thatthere are likely no issues relating to any industrial component of theindustrial manufacturing setting and the industrial manufacturingsetting. In some embodiments, compressing the one or more instances ofsensor data using the lossy codec includes: normalizing the one or moreinstances of sensor data into respective pixel values; encoding therespective pixel values into a video frame; and compressing a block ofvideo frames using the lossy codec, wherein the lossy codec is a videocodec and the block of video frames includes the video frame and one ormore other video frames that include normalized pixel values of otherinstances of sensor data. In embodiments, selectively encoding the oneor more instances of sensor data includes compressing the one or moreinstances of sensor data using a lossless codec in response to obtaininga prediction or classification relating to a condition of a particularindustrial component or the industrial manufacturing setting thatindicates that there is likely an issue relating to the particularindustrial component or the industrial manufacturing setting. Inembodiments, selectively encoding the one or more instances of sensordata includes refraining from compressing the one or more instances ofsensor data in response to obtaining a prediction or classificationrelating to a condition of a particular industrial component or theindustrial manufacturing setting that indicates that there is likely anissue relating to the particular industrial component or the industrialmanufacturing setting. In embodiments, performing one or more edgeoperations includes: generating a feature vector based on one or moreinstances of sensor data received from one or more sensors of theplurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the industrialmanufacturing setting or the industrial manufacturing setting and adegree of confidence corresponding to the prediction or classification;and selectively storing the one or more instances of sensor data in astorage device of the edge device based on the prediction orclassification. In embodiments, selectively storing the one or moreinstances of sensor data includes storing the one or more instances ofsensor data in the storage device with an expiry, such that the one ormore instances of sensor data are purged from the storage device inaccordance with the expiry in response to obtaining one or morepredictions or classifications relating to conditions of respectiveindustrial components of the industrial manufacturing setting and theindustrial manufacturing setting that collectively indicate that thereare likely no issues relating to any industrial component of theindustrial manufacturing setting and the industrial manufacturingsetting. In embodiments, selectively storing the one or more instancesof sensor data includes storing the one or more instances of sensor datain the storage device indefinitely in response to obtaining a predictionor classification relating to a condition of a particular industrialcomponent or the industrial manufacturing setting that indicates thatthere is likely an issue relating to the particular industrial componentor the industrial manufacturing setting.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is a meshnetwork such that: the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors; and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitfurther includes one or more collection devices configured to receivereporting packets from one or more sensors of the plurality of sensorsand route the reporting packets to the edge device. In embodiments, eachcollection device is installed in a different respective room of theindustrial manufacturing setting and collects sensor data from sensorsof the plurality sensors that are deployed in the respective room.

According to some embodiments of the present disclosure, a sensor kitconfigured to monitor an underwater industrial setting is disclosed. Thesensor kit includes an edge device and a plurality of sensors thatcapture sensor data and transmit the sensor data via a self-configuringsensor kit network, wherein the plurality of sensors includes one ormore sensors of a first sensor type and one or more sensors of a secondsensor type. At least one sensor of the plurality of sensors includes: asensing component that captures sensor measurements and outputsinstances of sensor data; a processing unit that generates reportingpackets based on one or more instances of sensor data and outputs thereporting packets, wherein each reporting packet includes routing dataand one or more instances of sensor data; and a communication deviceconfigured to receive reporting packets from the processing unit and totransmit the reporting packets to the edge device via theself-configuring sensor kit network in accordance with a firstcommunication protocol. The plurality of sensors includes two or moresensor types selected from the group including: infrared sensors, sonarsensors, LIDAR sensors, water penetrating sensors, light sensors, strainsensors, rust sensors, biological sensors, temperature sensors, chemicalsensors, valve integrity sensors, vibration sensors, flow sensors,cavitation sensors, pressure sensors, weight sensors, and camerasensors. The edge device includes a communication system having a firstcommunication device that receives reporting packets from the pluralityof sensors via the self-configuring sensor kit network and a secondcommunication device that transmits sensor kit packets to a backendsystem via a public network. The edge device further includes aprocessing system having one or more processors that executecomputer-executable instructions that cause the processing system to:receive the reporting packets from the communication system; perform oneor more edge operations on the instances of sensor data in the reportingpackets; generate the sensor kit packets based on the instances ofsensor data, wherein each sensor kit packet includes at least oneinstance of sensor data; and output the sensor kits packets to thecommunication system, wherein the communication system transmits thereporting packets to the backend system via the public network.

In some embodiments, the sensor kit further includes a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge device.In some of these embodiments, the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.Alternatively, in some embodiments, the gateway device includes acellular chipset that is pre-configured to transmit sensor kit packetsto a cellphone tower of a preselected cellular provider.

In some embodiments, the second communication device of the edge deviceis a satellite terminal device that is configured to transmit the sensorkit packets to a satellite that routes the sensor kits to the publicnetwork.

In embodiments, the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit.

In embodiments, the edge device further includes one or more storagedevices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the underwater industrialsetting and/or the underwater industrial setting based on a set offeatures that are derived from instances of sensor data captured by oneor more of the plurality of sensors. In some embodiments, performing oneor more edge operations includes: generating a feature vector based onone or more instances of sensor data received from one or more sensorsof the plurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the underwaterindustrial setting or the underwater industrial setting and a degree ofconfidence corresponding to the prediction or classification; andselectively encoding the one or more instances of sensor data prior totransmission to the backend system based on the condition or prediction.In embodiments, selectively encoding the one or more instances of sensordata includes compressing the one or more instances of sensor data usinga lossy codec in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the underwater industrial setting and the underwaterindustrial setting that collectively indicate that there are likely noissues relating to any industrial component of the underwater industrialsetting and the underwater industrial setting. In embodiments,compressing the one or more instances of sensor data using the lossycodec includes: normalizing the one or more instances of sensor datainto respective pixel values; encoding the respective pixel values intoa video frame; and compressing a block of video frames using the lossycodec, wherein the lossy codec is a video codec and the block of videoframes includes the video frame and one or more other video frames thatinclude normalized pixel values of other instances of sensor data. Inembodiments, selectively encoding the one or more instances of sensordata includes compressing the one or more instances of sensor data usinga lossless codec in response to obtaining a prediction or classificationrelating to a condition of a particular industrial component or theunderwater industrial setting that indicates that there is likely anissue relating to the particular industrial component or the underwaterindustrial setting. In embodiments, selectively encoding the one or moreinstances of sensor data includes refraining from compressing the one ormore instances of sensor data in response to obtaining a prediction orclassification relating to a condition of a particular industrialcomponent or the underwater industrial setting that indicates that thereis likely an issue relating to the particular industrial component orthe underwater industrial setting. In embodiments, performing one ormore edge operations includes: generating a feature vector based on oneor more instances of sensor data received from one or more sensors ofthe plurality of sensors; inputting the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the underwaterindustrial setting or the underwater industrial setting and a degree ofconfidence corresponding to the prediction or classification; andselectively storing the one or more instances of sensor data in astorage device of the edge device based on the prediction orclassification. In embodiments, selectively storing the one or moreinstances of sensor data includes storing the one or more instances ofsensor data in the storage device with an expiry in response toobtaining one or more predictions or classifications relating toconditions of respective industrial components of the underwaterindustrial setting and the underwater industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the underwater industrial setting and theunderwater industrial setting, such that the one or more instances ofsensor data are purged from the storage device in accordance with theexpiry. In embodiments, selectively storing the one or more instances ofsensor data includes storing the one or more instances of sensor data inthe storage device indefinitely in response to obtaining a prediction orclassification relating to a condition of a particular industrialcomponent or the underwater industrial setting that indicates that thereis likely an issue relating to the particular industrial component orthe underwater industrial setting.

In embodiments, the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is a meshnetwork such that: the communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors; and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the edge device. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the edge device to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the edge device initiatingconfiguration of the self-configuring sensor kit network.

In some embodiments, the self-configuring sensor kit network is ahierarchical network. In some of these embodiments, the sensor kitfurther includes one or more collection devices configured to receivereporting packets from one or more sensors of the plurality of sensorsand route the reporting packets to the edge device. In some of theseembodiments, wherein each collection device is installed in a differentrespective section of the underwater industrial setting and collectssensor data from sensors of the plurality sensors that are deployed inthe respective section.

According to some embodiments of the present disclosure, a system formonitoring an industrial setting is disclosed. The system includes a setof sensor kits each having a set of sensors that are registered torespective industrial settings and configured to monitor physicalcharacteristics of the industrial settings. The system also includes aset of communication gateway for communicating instances of sensorvalues from the sensor kits to a backend system. The backend system isconfigured to process the instances of sensor values to monitor theindustrial setting, wherein upon receiving registration data for asensor kit to an industrial setting, the backend system automaticallyconfigures and populates a dashboard for an owner or operator of theindustrial setting. The dashboard provides monitoring information thatis based on the instances of sensor values for the industrial setting.

In embodiments, the registration of the sensor kit includes an interfacefor specifying a type of entity or industrial setting to be monitored.In some of these embodiments, the backend system configures thedashboard based on the registered type of entity or industrial setting.In embodiments, the backend system includes an analytics facility thatis configured based on the type of entity or industrial setting. Inembodiments, the backend system includes a machine learning facilitythat is configured based on the type of entity or industrial setting.

In embodiments, the communication gateway is configured to provide avirtual container for instances of sensor values such that only aregistered owner or operator of the industrial setting can access thesensor values.

In embodiments, upon registration of a sensor kit to an industrialsetting, a user may select a set or parameters for monitoring andwherein a set of services and capabilities of the backend system isautomatically provisioned based on the selected parameters.

In embodiments, at least one of the sensor kit, the communicationgateway and the backend system includes an edge computation system forautomatically calculating a metric for an industrial setting based on aplurality of instances of sensor values from a set of sensor kits.

In embodiments, the sensor kit is a self-configuring sensor kit network.In some embodiments, the sensor kit network is a star network such thateach sensor of the plurality of sensors transmits respective instancesof sensor data with the communication gateway directly using ashort-range communication protocol. In some embodiments,computer-executable instructions cause one or more processors of thecommunication gateway device to initiate configuration of theself-configuring sensor kit network. In some embodiments, theself-configuring sensor kit network is a mesh network such that: acommunication device of each sensor of the plurality of sensors isconfigured to establish a communication channel with at least one othersensor of the plurality of sensors; and at least one sensor of theplurality of sensors is configured to receive instances of sensor datafrom one or more other sensors of the plurality of sensors and to routethe received instances of the sensor data towards the communicationgateway. In some embodiments, the computer-executable instructionsfurther cause the one or more processors of the communication gateway toinitiate configuration of the self-configuring sensor kit network,wherein the plurality of sensors form the mesh network in response tothe communication gateway initiating configuration of theself-configuring sensor kit network. In some embodiments, theself-configuring sensor kit network is a hierarchical network.

According to some embodiments of the present disclosure, a system formonitoring an industrial setting is disclosed. The system includes: aset of sensor kits each having a set of sensors that are registered torespective industrial settings and configured to monitor physicalcharacteristics of the industrial settings; a set of communicationgateways for communicating instances of sensor values from the sensorkits to a backend system; and said backend system for processing theinstances of sensor values to monitor the industrial setting, whereinupon receiving registration data for a sensor kit to an industrialsetting, the backend system automatically configures and populates adashboard for an owner or operator of the industrial setting, whereinthe dashboard provides monitoring information that is based on theinstances of sensor values for the industrial setting. In someembodiments, the registration of the sensor kit includes an interfacefor specifying a type of entity or industrial setting to be monitored.In embodiments, the backend system configures the dashboard based on theregistered type of entity or industrial setting. In some embodiments,the backend system includes an analytics facility that is configuredbased on the type of entity or industrial setting. In embodiments, thebackend system includes a machine learning facility that is configuredbased on the type of entity or industrial setting.

In some embodiments, the communication gateway is configured to providea virtual container for instances of sensor values such that only aregistered owner or operator of the industrial setting can access thesensor values. In embodiments, upon registration of a sensor kit to anindustrial setting, a user may select a set of parameters for monitoringand wherein a set of services and capabilities of the backend system isautomatically provisioned based on the selected parameters. In someembodiments, at least one of the sensor kit, the communication gatewayand the backend system includes an edge computation system forautomatically calculating a metric for an industrial setting based on aplurality of instances of sensor values from a set of sensor kits.

In some embodiments, the sensor kit is a self-configuring sensor kitnetwork. In some of these embodiments, the sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the communication gatewaydirectly using a short-range communication protocol. In embodiments,computer-executable instructions cause one or more processors of thecommunication gateway device to initiate configuration of theself-configuring sensor kit network.

In some embodiments, the self-configuring sensor kit network is a meshnetwork such that: a communication device of each sensor of theplurality of sensors is configured to establish a communication channelwith at least one other sensor of the plurality of sensors; and at leastone sensor of the plurality of sensors is configured to receiveinstances of sensor data from one or more other sensors of the pluralityof sensors and to route the received instances of the sensor datatowards the communication gateway. In some of these embodiments, thecomputer-executable instructions further cause the one or moreprocessors of the communication gateway to initiate configuration of theself-configuring sensor kit network, wherein the plurality of sensorsform the mesh network in response to the communication gatewayinitiating configuration of the self-configuring sensor kit network. Insome embodiments, the self-configuring sensor kit network is ahierarchical network.

According to some embodiments of the present disclosure, a method ofmonitoring a plurality of industrial settings using a set of sensorskits, a set of communication gateways, and a backend system isdisclosed. The method includes: registering each sensor kit of theplurality of sensor kits to a respective industrial setting of theplurality of industrial settings; configuring each sensor kit of theplurality of sensor kits to monitor physical characteristics of therespective industrial setting to which the sensor kit is registered;transmitting, by each communication gateway of the set of communicationgateways, instances of sensor data from a respective sensor kit of theplurality of sensor kits to the backend system; processing, by thebackend system, the instances of sensor data received from each sensorkit of the plurality of sensor kits; automatically configuring andpopulating, by the backend system, a dashboard for an owner or operatorof the respective industrial setting upon receiving registration datafor a sensor kit of the plurality of sensor kits; and providing, by thedashboard, monitoring information that is based on the instances ofsensor data for the respective industrial setting.

In some embodiments, registering each sensor kit includes providing aninterface for specifying a type of entity or industrial setting to bemonitored. In some of these embodiments, configuring each sensor kit tomonitor physical characteristics of the respective industrial settingincludes configuring, by the backend system, the dashboard based on theregistered type of entity or industrial setting. In some embodiments,the backend system includes an analytics facility that is configuredbased on the type of entity of the industrial setting. In embodiments,the backend system includes a machine learning facility that isconfigured based on the type of entity or industrial setting.

In some embodiments, the method further includes providing, by eachcommunication gateway of the plurality of communication gateways, avirtual container for instances of sensor data such that only aregistered owner or operator of the respective industrial setting canaccess the sensor data. In embodiments, upon registration of a sensorkit to an industrial setting, a user may select a set of parameters formonitoring. In some embodiments, the method further includesautomatically provisioning, by the backend system, a set of services andcapabilities of the backend system based on the selected parameters. Inembodiments, at least one of a sensor kit of the plurality of sensorkits, a communication gateway of the plurality of communicationgateways, and the backend system includes an edge computation system forautomatically calculating a metric for an industrial setting based on aplurality of instances of sensor data from a set of sensor kits.

In some embodiments, at least one sensor kit of the plurality of sensorkits is a self-configuring sensor kit network including a plurality ofsensors. In some of these embodiments, the method further includes:capturing, by the plurality of sensors, sensor data; and transmitting,by the plurality of sensors, the sensor data to and edge device via theself-configuring sensor kit network. In some embodiments, transmittingthe sensor data via the self-configuring sensor kit network includesdirectly transmitting, by each sensor of the plurality of sensors,instances of sensor data with the edge device using a short-rangecommunication protocol, wherein the self-configuring sensor kit networkis a star network. In some embodiments, the method further includesinitiating, by the edge processing system, configuration of theself-configuring sensor kit network.

In embodiments, the self-configuring sensor kit network is a meshnetwork and each sensor of the plurality of sensors includes acommunication device. In some of these embodiments, the method furtherincludes: establishing, by the communication device of each sensor ofthe plurality of sensors, a communication channel with at least oneother sensor of the plurality of sensors; receiving, by at least onesensor of the plurality of sensors, instances of sensor data from one ormore other sensors of the plurality of sensors; and routing, by the atleast one sensor of the plurality of sensors, the received instances ofthe sensor data towards the edge device.

In some embodiments, the self-configuring sensor kit network is ahierarchical network and the sensor kit includes one or more collectiondevices. In some embodiments, the plurality of sensors includes a firstset of sensors of a first sensor type and a second set of sensors of asecond sensor type.

According to some embodiments of the present disclosure, a sensor kitconfigured for monitoring an industrial setting is disclosed. The sensorkit includes: an edge device; and a plurality of sensors that capturesensor data and transmit the sensor data via a self-configuring sensorkit network, wherein the plurality of sensors includes one or moresensors of a first sensor type and one or more sensors of a secondsensor type, wherein at least one sensor of the plurality of sensorsincludes: a sensing component that captures sensor measurements andoutputs instances of sensor data; a processing unit that generatesreporting packets based on one or more instances of sensor data andoutputs the reporting packets, wherein each reporting packet includesrouting data and one or more instances of sensor data; and acommunication device configured to receive reporting packets from theprocessing unit and to transmit the reporting packets to the edge devicevia the self-configuring sensor kit network in accordance with a firstcommunication protocol. The edge device includes: a communication systemhaving: a first communication device that receives reporting packetsfrom the plurality of sensors via the self-configuring sensor kitnetwork; and a second communication device that transmits sensor kitpackets to a backend system via a public network; a processing systemhaving one or more processors that execute computer-executableinstructions that cause the processing system to: receive the reportingpackets from the communication system; generate a data block based onsensor data obtained from the reporting packets, wherein the data blockincludes (i) a block header that defines an address of the data blockand (ii) a block body that defines the sensor data and a parent addressof another data block to which the data block will be linked; andtransmit the data block to one or more node computing devices thatcollectively store a distributed ledger that is comprised of a pluralityof data blocks.

In some embodiments, generating the data block includes generating ahash value of the block body. In embodiments, generating the data blockincludes encrypting the block body.

In some embodiments, the distributed ledger includes a smart contractthat defines one or more conditions relating to collected sensor dataand one or more actions that are initiated by the smart contract inresponse to the one or more conditions being satisfied. In someembodiments, the smart contract receives the data block from the sensorkit and determines whether the one or more conditions are satisfiedbased on at least the sensor data stored in the data block. Inembodiments, the smart contract corresponds to an insurer. In someembodiments, the action defined in the smart contract triggers atransfer of funds to an account associated with an operator associatedwith the sensor kit in response to satisfying the one or moreconditions. In embodiments, the one or more conditions include a firstcondition that determines whether the sensor kit has reported asufficient amount of sensor data and a second condition that determineswhether the reported sensor data indicates that the industrial settingis operating without issue.

In some embodiments, the smart contract corresponds to a regulatorybody. In some of these embodiments, the action defined in the smartcontract triggers an issuance of a token to an operator associated withthe sensor kit in response to satisfying the one or more conditions. Inembodiments, the one or more conditions include a first condition thatrequires a certain amount of reported sensor data to be reported by asensor kit and a second condition that requires the reported sensor datato be compliant with the reporting regulations.

In some embodiments, the edge device is one of the node computingdevices.

According to some embodiments of the present disclosure, a method formonitoring an industrial setting using a sensor kit having a pluralityof sensors and an edge device including a processing system isdisclosed. The method includes: receiving, by the processing system,reporting packets from one or more respective sensors of the pluralityof sensors, wherein each reporting packet includes routing data and oneor more instances of sensor data; generating, by the processing system,a data block based on sensor data obtained from the reporting packets,wherein the data block includes (i) a block header that defines anaddress of the data block and (ii) a block body that defines the sensordata and a parent address of another data block to which the data blockwill be linked; and transmitting, by the processing system, the datablock to one or more node computing devices that collectively store adistributed ledger that is comprised of a plurality of data blocks. Insome embodiments, generating the data block includes generating, by theprocessing system, a hash value of the block body. In embodiments,generating the data block includes encrypting, by the processing system,the block body.

In some embodiments, the distributed ledger includes a smart contractthat defines one or more conditions relating to collected sensor dataand one or more actions that are initiated by the smart contract inresponse to the one or more conditions being satisfied. In some of theseembodiments, the smart contract receives the data block from the sensorkit and determines whether the one or more conditions are satisfiedbased on at least the sensor data stored in the data block. In someembodiments, the smart contract corresponds to an insurer. Inembodiments, the action defined in the smart contract triggers atransfer of funds to an account associated with an operator associatedwith the sensor kit in response to satisfying the one or moreconditions. In some embodiments, the one or more conditions include afirst condition that determines whether the sensor kit has reported asufficient amount of sensor data and a second condition that determineswhether the reported sensor data indicates that the industrial settingis operating without issue.

In some embodiments, the smart contract corresponds to a regulatorybody. In some of these embodiments, the action defined in the smartcontract triggers an issuance of a token to an operator associated withthe sensor kit in response to satisfying the one or more conditions.

In some embodiments, the one or more conditions include a firstcondition that requires a certain amount of reported sensor data to bereported by a sensor kit and a second condition that requires thereported sensor data to be compliant with the reporting regulations.

In some embodiments, the edge device is one of the node computingdevices.

In some embodiments, the plurality of sensors includes a first set ofsensors of a first sensor type and a second set of sensors of a secondsensor type.

According to some embodiments of the present disclosure, a system isdisclosed. The system includes: a backend system including one or moreservers configured to deploy a smart contract to a distributed ledger onbehalf of a user, wherein the smart contract defines one or moreconditions relating to collected sensor data and one or more actionsthat are initiated by the smart contract in response to the one or moreconditions being satisfied; a sensor kit configured for monitoring anindustrial setting, the sensor kit including: an edge device; and aplurality of sensors that capture sensor data and transmit the sensordata via a self-configuring sensor kit network, wherein the plurality ofsensors includes one or more sensors of a first sensor type and one ormore sensors of a second sensor type, wherein at least one sensor of theplurality of sensors includes: a sensing component that captures sensormeasurements and outputs instances of sensor data; a processing unitthat generates reporting packets based on one or more instances ofsensor data and outputs the reporting packets, wherein each reportingpacket includes routing data and one or more instances of sensor data;and a communication device configured to receive reporting packets fromthe processing unit and to transmit the reporting packets to the edgedevice via the self-configuring sensor kit network in accordance with afirst communication protocol. The edge device includes: a communicationsystem having a first communication device that receives reportingpackets from the plurality of sensors via the self-configuring sensorkit network, and a second communication device that transmits sensor kitpackets to a backend system via a public network; a processing systemhaving one or more processors that execute computer-executableinstructions that cause the processing system to: receive the reportingpackets from the communication system; generate a data block based onsensor data obtained from the reporting packets, wherein the data blockincludes (i) a block header that defines an address of the data blockand (ii) a block body that defines the sensor data and a parent addressof another data block to which the data block will be linked; andtransmit the data block to one or more node computing devices thatcollectively store a distributed ledger that is comprised of a pluralityof data blocks.

In some embodiments, generating the data block includes generating ahash value of the block body. In some embodiments, generating the datablock includes encrypting the block body.

In some embodiments, the smart contract receives the data block from thesensor kit and determines whether the one or more conditions aresatisfied based on at least the sensor data stored in the data block. Insome of these embodiments, the smart contract corresponds to an insurer.In some embodiments, the action defined in the smart contract triggers atransfer of funds to an account associated with an operator associatedwith the sensor kit in response to satisfying the one or moreconditions. In embodiments, the one or more conditions include a firstcondition that determines whether the sensor kit has reported asufficient amount of sensor data and a second condition that determineswhether the reported sensor data indicates that the industrial settingis operating without issue. In some embodiments, the smart contractcorresponds to a regulatory body. In embodiments, the action defined inthe smart contract triggers an issuance of a token to an operatorassociated with the sensor kit in response to satisfying the one or moreconditions. In some embodiments, the one or more conditions include acondition that determines whether the sensor kit has reported a requiredamount of sensor data as defined by a regulation.

In some embodiments, the edge device is one of the node computingdevices.

According to some embodiments of the present disclosure, a method formonitoring an industrial setting using a sensor kit in communicationwith a backend system, the sensor kit including a plurality of sensorsand an edge device, is disclosed. The method includes: deploying, by thebackend system, a smart contract to a distributed ledger on behalf of auser, wherein the smart contract defines one or more conditions relatingto collected sensor data and one or more actions that are initiated bythe smart contract in response to the one or more conditions beingsatisfied; receiving, by an edge processing system of the edge device,reporting packets from one or more respective sensors of the pluralityof sensors, wherein each reporting packet includes routing data and oneor more instances of sensor data; generating, by the edge processingsystem, a data block based on sensor data obtained from the reportingpackets, wherein the data block includes (i) a block header that definesan address of the data block and (ii) a block body that defines thesensor data and a parent address of another data block to which the datablock will be linked; and transmitting, by the edge processing system,the data block to one or more node computing devices that collectivelystore a distributed ledger that is comprised of a plurality of datablocks.

In some embodiments, generating the data block includes generating, bythe edge processing system, a hash value of the block body. Inembodiments, generating the data block includes encrypting, by the edgeprocessing system, the block body.

In some embodiments, the distributed ledger receives the data block fromthe sensor kit and determines whether the one or more conditions of thesmart contract are satisfied based on at least the sensor data stored inthe data block. In some of these embodiments, the smart contractcorresponds to an insurer. In embodiments, the action defined in thesmart contract triggers a transfer of funds to an account associatedwith an operator associated with the sensor kit in response tosatisfying the one or more conditions. In some embodiments, the one ormore conditions include a first condition that determines whether thesensor kit has reported a sufficient amount of sensor data and a secondcondition that determines whether the reported sensor data indicatesthat the industrial setting is operating without issue.

In some embodiments, the smart contract corresponds to a regulatorybody. In some of these embodiments, the action defined in the smartcontract triggers an issuance of a token to an operator associated withthe sensor kit in response to satisfying the one or more conditions. Insome embodiments, the one or more conditions include a condition thatdetermines whether the sensor kit has reported a required amount ofsensor data as defined by a regulation. In embodiments, the edge deviceis one of the node computing devices. In some embodiments, the backendsystem is one of the node computing devices. In embodiments, theplurality of sensors includes a first set of sensors of a first sensortype and a second set of sensors of a second sensor type.

A more complete understanding of the disclosure will be appreciated fromthe description and accompanying drawings and the claims, which follow.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a betterunderstanding of the disclosure, illustrate embodiment(s) of thedisclosure and together with the description serve to explain theprinciple of the disclosure. In the drawings:

FIG. 1 is a schematic illustrating an example of a sensor kit deployedin an industrial setting according to some embodiments of the presentdisclosure.

FIG. 2A is a schematic illustrating an example of a sensor kit networkhaving a star network topology according to some embodiments of thepresent disclosure.

FIG. 2B is a schematic illustrating an example of a sensor kit networkhaving a mesh network topology according to some embodiments of thepresent disclosure.

FIG. 2C is a schematic illustrating an example of a sensor kit networkhaving a hierarchical network topology according to some embodiments ofthe present disclosure.

FIG. 3A is a schematic illustrating an example of a sensor according tosome embodiments of the present disclosure.

FIG. 3B is a schematic illustrating an example schema of a reportingpacket according to some embodiments of the present disclosure.

FIG. 4 is a schematic illustrating an example of an edge device of asensor kit according to some embodiments of the present disclosure.

FIG. 5 is a schematic illustrating an example of a backend system thatreceives sensor data from sensor kits deployed in industrial settingsaccording to some embodiments of the present disclosure.

FIG. 6 is a flow chart illustrating an example set of operations of amethod for encoding sensor data captured by a sensor kit according tosome embodiments of the present disclosure.

FIG. 7 is a flow chart illustrating an example set of operations of amethod for decoding sensor data provided to a backend system by a sensorkit according to some embodiments of the present disclosure.

FIG. 8 is a flow chart illustrating an example set of operations of amethod for encoding sensor data captured by a sensor kit using a mediacodec according to some embodiments of the present disclosure.

FIG. 9 is a flow chart illustrating an example set of operations of amethod for decoding sensor data provided to a backend system by a sensorkit using a media codec according to some embodiments of the presentdisclosure.

FIG. 10 is a flow chart illustrating an example set of operations of amethod for determining a transmission strategy and/or a storage strategyfor sensor data collected by a sensor kit based on the sensor data,according to some embodiments of the present disclosure

FIGS. 11-15 are schematics illustrating different configurations ofsensor kits according to some embodiments of the present disclosure.

FIG. 16 is a flowchart illustrating an example set of operations of amethod for monitoring industrial settings using an automaticallyconfigured backend system, according to some embodiments of the presentdisclosure.

FIG. 17 is a plan view of a manufacturing facility illustrating anexemplary implementation of a sensor kit including an edge device,according to some embodiments of the present disclosure.

FIG. 18 is a plan view of a surface portion of an underwater industrialfacility illustrating an exemplary implementation of a sensor kitincluding an edge device, according to some embodiments of the presentdisclosure.

FIG. 19 is a plan view of an indoor agricultural facility illustratingan exemplary implementation of a sensor kit including an edge device,according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

Various configurations of sensor kits are disclosed. A sensor kit may bea purpose-configured system that includes sensors for monitoring aspecific type of industrial setting, wherein the sensors are provided ina unified kit, optionally along with other devices, systems andcomponents, such as ones that provide communication, processing andintelligence capabilities. In embodiments, an owner or operator of anindustrial setting may purchase or otherwise obtain the sensor kit.During the purchase process, the owner or operator, or a user associatedwith the industrial setting, may provide or indicate one or morefeatures of the industrial setting (e.g., type of the setting, locationof the setting, size of the setting, whether the setting is indoors oroutdoors, the components and/or types of components being monitored, thenumber of each component and/or type of component being monitored, andthe like). In embodiments, the sensor kit may be preconfigured based onfeatures and requirements of the industrial operator or owner. Thesensor kit may be preconfigured such that the owner or operator mayinstall the sensor kit in a “plug-and-play” manner, whereby the owner oroperator does not need to configure a sensor kit network on which thedevices of the sensor kit communicate.

FIG. 1—Sensor Kit Environment

FIG. 1 is a schematic illustrating an industrial setting 120 at which asensor kit 100 has been installed. In embodiments, the sensor kit 100may refer to a fully deployable, purpose-configured industrial IoTsystem that is provided in a unified kit and is ready for deployment inthe industrial setting 120 by a consumer entity (e.g., owner or operatorof an industrial setting 120). In embodiments, the sensor kit 100 allowsthe owner or operator to install and deploy the sensor kit with no orminimal configuration (e.g., setting user permissions, settingpasswords, and/or setting notification and/or display preferences). Theterm “sensor kit” 100 may refer to a set of devices that are installedin an industrial setting 120 (e.g., a factory, a mine, an oil field, anoil pipeline, a refinery, a commercial kitchen, an industrial complex, astorage facility, a building site, and the like). The collection ofdevices comprising the sensor kit 100 includes a set of one or moreinternet of things (IoT) sensors 102 and a set of one or more edgedevices 104. For purposes of discussion, references to “sensors” or“sensor devices” should be understood to mean IoT sensors, unlessspecifically stated otherwise.

In embodiments, the sensor kit 100 includes a set of IoT sensors 102that are configured for deployment in, on, or around an industrialcomponent, a type of an industrial component (e.g., a turbine, agenerator, a fan, a pump, a valve, an assembly line, a pipe or pipeline,a food inspection line, a server rack, and the like), an industrialsetting 120, and/or a type of industrial setting 120 (e.g., indoor,outdoor, manufacturing, mining, drilling, resource extraction,underground, underwater, and the like) and a set of edge devices capableof handling inputs from the sensors and providing network-basedcommunications. In embodiments, an edge device 104 may include or maycommunicate with a local data processing system (e.g., a deviceconfigured to compress sensor data, filter sensor data, analyze sensordata, issue notifications based on sensor data and the like) capable ofproviding local outputs, such as of signals and of analytic results thatresult from local processing. In embodiments, the edge device 104 mayinclude or may communicate with a communication system (e.g., a Wi-Fichipset, a cellular chipset, a satellite transceiver, cognitive radio,one or more Bluetooth chips and/or other networking device) that iscapable of communicating data (e.g., raw and/or processed sensor data,notifications, command instructions, etc.) within and outside theindustrial environment. In embodiments, the communication system isconfigured to operate without reliance on the main data or communicationnetworks of an industrial setting 120. In embodiments, the communicationsystem is provided with security capabilities and instructions thatmaintain complete physical and data separation from the main data orcommunication networks of an industrial setting 120. For example, inembodiments, Bluetooth-enabled edge devices may be configured to permitpairing only with pre-registered components of a kit, rather than withother Bluetooth-enabled devices in an industrial setting 120.

In embodiments, an IoT sensor 102 is a sensor device that is configuredto collect sensor data and to communicate sensor data to another deviceusing at least one communication protocol. In embodiments, IoT sensors102 are configured for deployment in, on, or around a defined type of anindustrial entity. The term industrial entity may refer to any objectthat may be monitored in an industrial setting 120. In embodiments,industrial entities may include industrial components (e.g., a turbine,a generator, a fan, a pump, a valve, an assembly line, a pipe or pipeline, a food inspection line, a server rack, and the like). Inembodiments, industrial entities may include organisms that areassociated with an industrial setting 120 (e.g., humans working in theindustrial setting 120 or livestock being monitored in the industrialsetting 120). Depending on the intended use, setting, or purpose of thesensor kit 100, the configuration and form factor of an IoT sensor 102will vary. Examples of different types of sensors include: vibrationsensors, inertial sensors, temperature sensors, humidity sensors, motionsensors, LIDAR sensors, smoke/fire sensors, current sensors, pressuresensors, pH sensors, light sensors, radiation sensors, and the like.

In embodiments, an edge device 104 may be a computing device configuredto receive sensor data from the one or more IoT sensors 102 and performone or more edge-related processes relating to the sensor data. Anedge-related process may refer to a process that is performed at an edgedevice 104 in order to store the sensor data, reduce bandwidth on acommunication network, and/or reduce the computational resourcesrequired at a backend system. Examples of edge processes can includedata filtering, signal filtering, data processing, compression,encoding, quick-predictions, quick-notifications, emergency alarming,and the like.

In embodiments, a sensor kit 100 is pre-configured such that the devices(e.g., sensors 102, edge devices 104, collection devices, gateways,etc.) within the sensor kit 100 are configured to communicate with oneanother via a sensor kit network without a user having to configure thesensor kit network. A sensor kit network may refer to a closedcommunication network that is established between the various devices ofthe sensor kit and that utilizes two or more different communicationprotocols and/or communication mediums to enable communication of databetween the devices and to a broader communication network, such as apublic communication network 190 (e.g., the Internet, a satellitenetwork, and/or one or more cellular networks). For example, while somedevices in a sensor kit network may communicate using a Bluetoothcommunication protocol, other devices may communicate with one anotherusing a near-field communication protocol, a Zigbee protocol, and/or aWi-Fi communication protocol. In some implementations, a sensor kit 100may be configured to establish a mesh network having various devicesacting as routing nodes within the sensor kit network. For example,sensors 102 may be configured to collect data and transmit the collecteddata to the edge device 104 via the sensor kit network, but may also beconfigured to receive and route data packets from other sensors 102within the sensor kit network towards an edge device 104.

In embodiments, a sensor kit network may include additional types ofdevices. In embodiments, a sensor kit 100 may include one or morecollection devices (not shown in FIG. 1) that act as routing nodes inthe sensor network, such that the collection devices may be part of amesh network. In embodiments, a sensor kit 100 may include a gatewaydevice (not shown in FIG. 1) that enable communication with a broadernetwork, whereby the gateway device may communicate with the edge device104 over a wired or wireless communication medium in industrial settings120 that would prevent an edge device 104 from communicating with thepublic communication network 190 (e.g., in a factory having very thickconcrete walls). Embodiments of the sensor kit 100 may includeadditional devices without departing from the scope of the disclosure.

In embodiments, the sensor kit 100 is configured to communicate with abackend system 150 via a communication network, such as the publiccommunication network 190. In embodiments, the backend system 150 isconfigured to receive sensor data from a sensor kit 100 and to performone or more backend operations on the received sensor data. Examples ofbackend operations may include storing the sensor data in a database,performing analytics tasks on the sensor data, providing the results ofthe analytics and/or visualizations of the sensor data to a user via aportal and/or a dashboard, training one or more machine-learned modelsusing the sensor data, determining predictions and/or classificationsrelating to the operation of the industrial setting 120 and/orindustrial devices of the industrial setting 120 based on the sensordata, controlling an aspect and/or an industrial device of theindustrial setting 120 based on the predictions and/or classifications,issuing notifications to the user via the portal and/or the dashboardbased on the predictions and/or classifications, and the like.

It is appreciated that in some embodiments, the sensor kit 100 mayprovide additional types of data to the backend system 150. For example,the sensor kit 100 may provide diagnostic data indicating any detectedissues (e.g., malfunction, battery levels low, etc.) or potential issueswith the sensors 102 or other devices in the sensor kit 100.

In embodiments, the sensor kit 100 is configured to self-monitor forfailing components (e.g., failing sensors 102) and to report failingcomponents to the operator. For example, in some embodiments, the edgedevice 104 may be configured to detect failure of a sensor 102 based ona lack of reporting from a sensor, a lack of response to requests (e.g.,“pings”), and/or based on unreliable data (e.g., data regularly fallingout of the expected sensor readings). In some embodiments, the edgedevice 104 can maintain a sensor kit network map indicating where eachdevice in the sensor kit network is located and can provide approximatelocations and/or identifiers of failed sensors to a user.

In embodiments, the sensor kit 100 may be implemented to allowpost-installation configuration. A post-installation configuration mayrefer to an update to the sensor kit 100 by adding devices and/orservices to the sensor kit 100 after the sensor kit 100 has beeninstalled. In some of these embodiments, users (e.g., operators of theindustrial setting 120) of the system may subscribe to or purchasecertain edge “services.” For example, the sensor kit 100 may beconfigured to execute certain programs installed on one or more devicesof the sensor kit 100 only if the user has a valid subscription orownership permission to access the edge service supported by theprogram. When the user no longer has the valid subscription and/orownership permission, the sensor kit 100 may preclude execution of thoseprograms. For example, a user may subscribe to unlock AI-based edgeservices, mesh networking capabilities, self-monitoring services,compression services, in-facility notifications, and the like.

In some embodiments, users can add new sensors 102 to the sensor kitpost-installation in a plug-and-play-like manner. In some of theseembodiments, the edge device 104 and the sensors 102 (or other devicesto be added to the sensor kit 100) may include respective short-rangecommunication capabilities (e.g., near-field communication (NFC) chips,RFID chips, Bluetooth chips, Wi-Fi adapters, and the like). In theseembodiments, the sensors 102 may include persistent storage that storesidentifying data (e.g., a sensor identifier value) and any other datathat would be used to add the sensor 102 to the sensor kit 100 (e.g., anindustrial device type, supported communication protocols, and thelike). In some embodiments, a user may initiate a post-installationaddition to the sensor kit 100 by pressing a button on the edge device104, and/or by bringing the sensor 102 into the vicinity of the edgedevice 104. In some embodiments, in response to a user initiating apost-installation addition to the sensor kit, the edge device 104 mayemit a signal (e.g., a radio frequency). The edge device 104 may emitthe signal, for example, as a result of a human user pushing a button orat a predetermined time interval. The emitted signal may trigger asensor 102 proximate enough to receive the signal and to transmit thesensor ID of the sensor 102 and any other suitable configuration data(e.g., device type, communication protocols, and the like). In responseto the sensor 102 transmitting its configuration data (e.g., sensor IDand other relevant configuration data) to the edge device 104, the edgedevice 104 may add the sensor 102 to the sensor kit 102. Adding thesensor 102 to the sensor kit 104 may include updating a data store ormanifest stored at the edge device 104 that identifies the devices ofthe sensor kit 100 and data relating thereto. Non-limiting examples ofdata that may be stored in the manifest relating to each respectivesensor 102 may include the communication protocol used by the sensor 102to communicate with the edge device 104 (or intermediate devices), thetype of sensor data provided by the sensor 102 (e.g., vibration sensordata, temperature data, humidity data, etc.), models used to analyzesensor data from the sensor 102 (e.g., a model identifier), alarm limitsassociated with the sensor 102, and the like.

In embodiments, the sensor kit 100 (e.g., the edge device 104) may beconfigured to update a distributed ledger 162 with sensor data capturedby the sensor kit 100. In embodiments, a distributed ledger 162 is aBlockchain or any other suitable distributed ledger 162. The distributedledger 162 may be a public ledger or a private ledger. Private ledgersreduce power consumption requirements of maintaining the distributedledger 162, while public ledgers consume more power but offer morerobust security. In embodiments, the distributed ledger 162 may bedistributed amongst a plurality of node computing devices 160. The nodecomputing devices 160 may be any suitable computing device, includingphysical servers, virtual servers, personal computing devices, and thelike. In some embodiments, the node computing devices 160 are approved(e.g., via a consensus mechanism) before the node computing devices 160may participate in the distributed ledger. In some embodiments, thedistributed ledger 162 may be privately stored. For example, adistributed ledger may be stored amongst a set of preapproved nodecomputing devices, such that the distributed ledger 162 is notaccessible by non-approved devices. In some embodiments, the nodecomputing devices 160 are edge devices 104 of the sensor kit 102 andother sensor kits 102.

In embodiments, the distributed ledger 162 is comprised of a set oflinked data structures (e.g., blocks, data records, etc.), such that thelinked data structures form an acyclic graph. For purposes ofexplanation, the data structures will be referred to as blocks. Inembodiments, each block may include a header that includes a unique IDof the block and a body that includes the data that is stored in theblock, and a pointer. In embodiments, the pointer is the block ID of aparent block of the block, wherein the parent block is a block that wascreated prior to the block being written. The data stored in arespective block can be sensor data captured by a respective sensor kit100. Depending on the implementation, the types of sensor data and theamount of sensor data stored in a respective body of a block may vary.For example, a block may store a set of sensor measurements from one ormore types of sensors 102 of the sensor kit 100 captured over a periodof time (e.g., sensor data 102 captured from all of the sensors 102 inthe sensor kit 100 over a period one hour or one day) and metadatarelating thereto (e.g., sensor identifiers of each sensor measurementand a timestamp of each sensor measurement or group of sensormeasurements). In some embodiments, a block may store sensormeasurements determined to be anomalous (e.g., outside a standarddeviation of expected sensor measurements or deltas in sensormeasurements that are above a threshold) and/or sensor measurementsindicative of an issue or potential issue, and related metadata (e.g.,sensor IDs of each sensor measurement and a timestamp of each sensormeasurement or group of sensor measurements). In some embodiments, thesensor data stored in a block may be compressed and/or encoded sensordata, such that the edge device 104 compresses/encodes the sensor datainto a more compact format. In embodiments, the edge device 104 maygenerate a hash of the body, such that the contents of the body (e.g.,block ID of the parent block and the sensor data) are hashed and cannotbe altered without changing the value of the hash. In embodiments, theedge device 104 may encrypt the content within the block, so that thecontent may not be read by unauthorized devices.

As mentioned, the distributed ledger 162 may be used for differentpurposes. In some embodiments, the distributed ledger 162 may furtherinclude one or more smart contracts. A smart contract is aself-executing digital contract. A smart contract may include code(e.g., executable instructions) that defines one or more conditions thattrigger one or more actions. A smart contract may be written by adeveloper in a scripting language (e.g., JavaScript), an object codelanguage (e.g., Java), or a compiled language (e.g., C++ or C). Oncewritten, a smart contract may be encoded in a block and deployed to thedistributed ledger 162. In embodiments, the backend system 150 isconfigured to receive the smart contract from a user and write the smartcontract to a respective distributed ledger 162. In embodiments, anaddress of the smart contract (e.g., the block ID of the blockcontaining the smart contract) may be provided to one or more parties tothe smart contract, such that respective parties may invoke the smartcontract using the address. In some embodiments, the smart contract mayinclude an API that allows a party to provide data (e.g., addresses ofblocks) and/or to transmit data (e.g., instructions to transfer funds toan account).

In example implementations, an insurer may allow insured owners and/oroperators of an industrial setting 120 to agree to share sensor datawith the insurer to demonstrate that the equipment in the facility isfunctioning properly and, in return, the insurer may issue a rebate orrefund to the owners and/or operators if the owners and/or operators arecompliant with an agreement with the insurers. Compliance with theagreement may be verified electronically by participant nodes in thedistributed ledger and/or the sensor kit 100 via a smart contract. Inembodiments, the insurer may deploy the smart contract (e.g., by addingthe smart contract to a distributed ledger 162) that triggers theissuance of rebates or refunds on portions of insurance premiums whenthe sensor kit 100 provides sufficient sensor data to the insurer viathe distributed ledger that indicates the facility is operating withoutissue. In some of these embodiments, the smart contract may include afirst condition that requires a certain amount of sensor data to bereported by a facility and a second condition that each instance of thesensor data equals a value (e.g., there are no classified or predictedissues) or range of values (e.g., all sensor measurements are within apredefined range of values). In some embodiments, the action taken inresponse to one or more of the conditions being met may be to depositfunds (e.g., a wire transfer or cryptocurrency) into an account. In thisexample, the edge device 104 may write blocks containing sensor data tothe distributed ledger. The edge device 104 may also provide theaddresses of these blocks to the smart contract (e.g., using an API ofthe smart contract). Upon the smart contract verifying the first andsecond conditions of the contract, the smart contract may initiate thetransfer of funds from an account of the insurer to the account of theinsured.

In another example, a regulatory body (e.g., a state, local, or federalregulatory agency) may require facility operators to report sensor datato ensure compliance with one or more regulations. For instance, theregulatory body may regulate food inspection facilities, pharmaceuticalmanufacturing facilities, e.g., manufacturing facility 1700, indooragricultural facilities, e.g., indoor agricultural facility 1800,offshore oil extraction facilities, e.g., underwater industrial facility1900, or the like. In embodiments, the regulatory body may deploy asmart contract that is configured to receive and verify the sensor datafrom an industrial setting 120, and in response to verifying the sensordata issues a compliance token (or certificate) to an account of thefacility owner. In some of these embodiments, the smart contract mayinclude a condition that requires a certain amount of sensor data to bereported by a facility and a second condition that requires the sensordata to be compliant with the reporting regulations. In this example,the edge device 104 may write blocks containing sensor data to thedistributed ledger 162. The edge device 104 may also provide theaddresses of these blocks to the smart contract (e.g., using an API ofthe smart contract). Upon the smart contract verifying the first andsecond conditions of the contract, the smart contract may generate atoken indicating compliance by the facility operator and may initiatethe transfer of funds to an account (e.g., a digital wallet) associatedwith the facility.

A distributed ledger 162 may be adapted for additional or alternativeapplications without departing from the scope of the disclosure.

FIGS. 2A, 2B, and 2C—Components and Networking

FIGS. 2A, 2B, and 2C illustrate example configurations of a sensor kitnetwork 200.

Depending on the sensor kit 100 and the industrial setting 120 that thesensor kit 100 is installed in, the sensor kit network 200 maycommunicate in different manners.

FIG. 2A illustrates an example sensor kit network 200A that is a starnetwork. In these embodiments, the sensors 102 communicate directly withthe edge device 104. In these embodiments, the communication protocol(s)utilized by the sensor devices 102 and the edge device 104 tocommunicate are based on one or more of the physical area of the sensorkit network 102, the power sources available, and the types of sensors102 in the sensor kit 100. For example, in settings where the area beingmonitored is a relatively small area and where the sensors 102 are notable to connect to a power supply, the sensors 102 may be fabricatedwith a Bluetooth Low Energy (BLE) microchip that communicates using aBluetooth Low Energy protocol (e.g., the Bluetooth 5 protocol maintainedby the Bluetooth Special Interest Group). In another example, in arelatively small area where lots of sensors 102 are to be deployed, thesensors 102 may be fabricated with the Wi-Fi microchip that communicatesusing the IEEE 802.11 protocol. In the embodiments of FIG. 2A, thesensors 102 may be configured to perform one-way or two-waycommunication. In embodiments where the edge device 104 does not need tocommunicate data and/or instructions to the sensors 102, the sensors 102may be configured for one-way communication. In embodiments where theedge device 104 does communicate data and/or instructions to the sensors102, the sensors 102 may be configured with transceivers that performtwo-way communication. A star network may be configured with deviceshaving other suitable communication devices without departing from thescope of the disclosure.

FIG. 2B illustrates an example sensor kit network 200B that is a meshnetwork where the nodes (e.g., sensors 102) connect to each otherdirectly, dynamically, and/or non-hierarchically to cooperate with oneanother to efficiently route data to and from the edge device 104. Insome embodiments, the devices in the mesh network (e.g., the sensors102, the edge device 104, and/or any other devices in the sensor kitnetwork 200B) may be configured to self-organize and self-configure themesh network, such that the sensors 102 and/or the edge device 104 maydetermine which devices route data on behalf of other devices, and/orredundancies for transmission should a routing node (e.g., sensor 102)fail. In embodiments, the sensor kit 100 may be configured to implementa mesh network in industrial settings 120 where the area being monitoredis relatively large (e.g., greater than 100 meters in radius from theedge device 104) and/or where the sensors 102 in the sensor kit 100 areintended to be installed in close proximity to one another. In thelatter scenario, the power consumption of each individual sensor 102 maybe reduced in comparison to sensors 102 in a star network, as thedistance that each respective sensor 102 needs to transmit over isrelatively less than the distance that the respective sensor 102 wouldneed to transmit over in a star network. In embodiments, a sensor 102may be fabricated with a Zigbee® microchips, a Digi XBee® microchip, aBluetooth Low Energy microchip, and/or any other suitable communicationdevices configured to participate in a mesh network.

FIG. 2C illustrates an example of a sensor kit network 200C that is ahierarchical network. In these embodiments, the sensor kit 100 includesa set of collection devices 206. A collection device 206 may refer to anon-sensor device that receives sensor data from a sensor device 104 androutes the sensor data to an edge device 104, either directly or viaanother collection device 206. In embodiments, a hierarchical networkmay refer to a network topography where one or more intermediate devices(e.g., collection devices 206) route data from one or more respectiveperipheral devices (e.g., sensor devices 102) to a central device (e.g.,edge device 104). A hierarchical network may include wired and/orwireless connections. In embodiments, a sensor device 102 may beconfigured to communicate with a collection device 206 via any suitablecommunication device (e.g., Bluetooth Low Energy microchips, Wi-Fimicrochips, Zigbee microchips, or the like). In embodiments,hierarchical sensor kit networks may be implemented in industrialsettings 120 where power sources are available to power the collectiondevices 206 and/or where the sensors 102 are likely to be spaced too farapart to support a reliable mesh network.

The examples of FIGS. 2A-2C are provided for examples of differenttopologies of a sensor kit network. These examples are not intended tolimit the types of sensor kit networks 200 that may be formed by asensor kit 100. Furthermore, sensor kit networks 200 may be configuredas hybrids of star networks, hierarchical networks, and/or meshnetworks, depending on the industrial settings 120 in which respectivesensor kits 200 are being deployed.

FIGS. 3A, 3B, 4, and 5—Example Configurations of Sensors, Edge Devices,and Backend Systems

FIG. 3A illustrates an example IoT sensor 102 (or sensor) according toembodiments of the present disclosure. Embodiments of the IoT sensor 102may include, but are not limited to, one or more sensing components 302,one or more storage devices 304, one or more power supplies 306, one ormore communication devices 308, and a processing device 310. Inembodiments, the processing device 310 may execute an edge reportingmodule 312.

A sensor 102 includes at least one sensing component 302. A sensingcomponent 302 may be any digital, analog, chemical, and/or mechanicalcomponent that outputs raw sensor data to the processing device 310. Itis appreciated that different types of sensors 102 are fabricated withdifferent types of sensing components. In embodiments, sensingcomponents 302 of an inertial sensor may include one or moreaccelerometers and/or one or more gyroscopes. In embodiments, sensingcomponents 302 of a temperature sensor may include one or morethermistors or other temperature sensing mechanisms. In embodiments,sensing components 302 of a heat flux sensor may include, for example,thin film sensors, surface mount sensors, polymer-based sensors,chemical sensors and others. In embodiments, sensing components 302 of amotion sensor may include a LIDAR device, a radar device, a sonardevice, or the like. In embodiments, sensing components 302 of anoccupancy sensor may include a surface being monitored for occupancy, apressure activated switch embedded under the surface of the occupancysensor and/or a piezoelectric element integrated into the surface of theoccupancy sensor, such that an electrical signal is generated when anobject occupies the surface being monitored for occupancy. Inembodiments, sensing components 302 of a humidity sensor may include acapacitive element (e.g., a metal oxide between to electrodes) thatoutputs an electrical capacity value corresponding to the ambienthumidity; a resistive element that includes a salt medium havingelectrodes on two sides of the medium, whereby the variable resistancemeasured at the electrodes corresponds to the ambient humidity; and/or athermal element that includes a first thermal sensor that outputs atemperature of a dry medium (e.g., dry nitrogen) and a second thermalsensor that outputs an ambient temperature of the sensor's environment,such that the humidity is determined based on the change, i.e., thedelta, between the temperature in the dry medium and the ambienttemperature. In embodiments, sensing components 302 of a vibrationsensor may include accelerometer components, position sensingcomponents, torque sensing components, and others. It is appreciatedthat the list of sensor types and sensing components thereof is providedfor example. Additional or alternative types of sensors and sensingcomponents may be integrated into a sensor 102 without departing fromthe scope of the disclosure. Furthermore, in some embodiments, thesensors 102 of a sensor kit 100 may include audio, visual, oraudio/visual sensors, in addition to non-audio/visual sensors 102 (i.e.,sensors that do not capture video or audio). In these embodiments, thesensing components 392 may include a camera and/or one or moremicrophones. In some embodiments, the microphones may be directionalmicrophones, such that a direction of a source of audio may bedetermined.

A storage device 304 may be any suitable medium for storing data that isto be transmitted to the edge device 104. In embodiments, a storagedevice 304 may be a persistent storage medium, such as a flash memorydevice. In embodiments, a storage device 304 may be a transitory storagemedium, such as a random access memory device. In embodiments, a storagedevice 304 may be a circuit configured to store charges, whereby themagnitude of the charge stored by the component is indicative of asensed value, or incremental counts. In these embodiments, this type ofstorage device 304 may be used where power availability and size areconcerns, and/or where the sensor data is count-based (e.g., a number ofdetection events). It is appreciated that any other suitable storagedevices 304 may be used. In embodiments, the storage device 304 mayinclude a cache 314, such that the cache 314 stores sensor data that isnot yet reported to the edge device 104. In these embodiments, the edgereporting module 312 may clear the cache 314 after the sensor data beingstored in the cache 314 is transmitted to the edge device 104.

A power supply 306 is any suitable component that provides power to theother components of the sensor 102, including the sensing components302, storage devices 304, communication devices 306, and/or theprocessing device 308. In embodiments, a power supply 306 includes awired connection to an external power supply (e.g., alternating currentdelivered from a power outlet, or direct current delivered from abattery or solar power supply). In embodiments, the power supply 306 mayinclude a power inverter that converts alternating currents to directcurrents (or vice-versa). In embodiments, a power supply 306 may includean integrated power source, such as a rechargeable lithium ion batteryor a solar element. In embodiments, a power supply 306 may include aself-powering element, such as a piezoelectric element. In theseembodiments, the piezoelectric element may output a voltage upon asufficient mechanical stress or force being applied to the element. Thisvoltage may be stored in a capacitor and/or may power a sensing element302. In embodiments, the power supply may include an antenna (e.g., areceiver or transceiver) that receives a radio frequency that energizesthe sensor 102. In these embodiments, the radio frequency may cause thesensor 102 to “wake up” and may trigger an action by the sensor 102,such as taking sensor measurements and/or reporting sensor data to theedge device 104. A power supply 306 may include additional oralternative components as well.

In embodiments, a communication device 308 is a device that enableswired or wireless communication with another device in the sensor kitnetwork 200. In most sensor kit configurations 100, the sensors 102 areconfigured to communicate wirelessly. In these embodiments, acommunication device 308 may include a transmitter or transceiver thattransmits data to other devices in the sensor kit network 200.Furthermore, in some of these embodiments, communication devices 308having transceivers may receive data from other devices in the sensorkit network 200. In wireless embodiments, the transceiver may beintegrated into a chip that is configured to perform communication usinga respective communication protocol. In some embodiments, acommunication device 308 may be a Zigbee® microchip, a Digi XBee®microchip, a Bluetooth microchip, a Bluetooth Low Energy microchip, aWi-Fi microchip, or any other suitable short-range communicationmicrochip. In embodiments where the sensor kit 200 supports a meshnetwork, the communication device 308 may be a microchip that implementsa communication protocol that supports mesh networking (e.g., ZigBee PROmesh networking protocol, Bluetooth Mesh, 802.11a/b/g/n/ac, and thelike). In these embodiments, a communication device 308 may beconfigured to establish the mesh network and handle the routing of datapackets received from other devices in accordance with the communicationprotocol implemented by the communication device 308. In someembodiments, a sensor 102 may be configured with two or morecommunication devices 308. In these embodiments, the sensors 102 may beadded to different sensor kit 100 configurations and/or may allow forflexible configuration of the sensor kit 102 depending on the industrialsetting 120.

In embodiments, the processing device 310 may be a microprocessor. Themicroprocessor may include memory (e.g., read-only memory (ROM)) thatstores computer-executable instructions and one or more processors thatexecute the computer-executable instructions. In embodiments, theprocessing device 310 executes an edge reporting module 312. Inembodiments, the edge reporting module 312 is configured to transmitdata to the edge device 104. Depending on the configuration of thesensor kit network 200 and location of the sensors 102 with respect tothe edge device 104, the edge reporting module 312 may transmit data(e.g., sensor data) either directly to the edge device 104, or to anintermediate device (e.g., a collection device 206 or another sensordevice 102) that routes the data towards the edge device 104. Inembodiments, the edge reporting module 312 obtains raw sensor data froma sensing component 302 or from a storage device 304 and packetizes theraw sensor data into a reporting packet 320.

FIG. 3B illustrates an example reporting packet 320 according to someembodiments of the present disclosure. In some of these embodiments, theedge reporting module 312 may populate a reporting packet template toobtain a reporting packet 320. In embodiments, a reporting packet 320may include a first field 322 indicating a sensor ID of the sensor 102and a second field 326 indicating the sensor data. Additionally, thereporting packet 320 may include additional fields, such as a routingdata field 324 indicating a destination of the packet (e.g., an addressor identifier of the edge device 104), a time stamp field 328 indicatinga time stamp, and/or a checksum field 330 indicating a checksum (e.g., ahash value of the contents of the reporting packet). The reportingpacket may include additional or alternative fields (e.g., error codes)without departing from the scope of the disclosure.

Referring back to FIG. 3A, in embodiments, the edge reporting module 312may generate a reporting packet 320 for each instance of sensor data.Alternatively, the edge reporting module 312 may generate a reportingpacket 320 that includes a batch of sensor data (e.g., the previous Nsensor readings or all the sensor readings maintained in a cache 314 ofthe sensor 102 since the cache 314 was last purged). Upon generating areporting packet 320, the edge reporting module 312 may output thereporting packet 320 to the communication device 308, which transmitsthe reporting packet 320 to the edge device 104 (either directly or viaone or more intermediate devices). The edge reporting module 312 maygenerate and transmit reporting packets 320 at predetermined intervals(e.g., every second, every minute, every hour), continuously, or uponbeing triggered (e.g., upon being activated via the power supply or uponbeing command by the edge device 104).

In embodiments, the edge reporting module 312 instructs the sensingcomponent(s) 302 to capture sensor data. In embodiments, the edgereporting module 312 may instruct a sensing component 302 to capturesensor data at predetermined intervals. For example, the edge reportingmodule 312 may instruct the sensing component 302 to capture sensor dataevery second, every minute, or every hour. In embodiments, the edgereporting module 312 may instruct a sensing component 302 to capturesensor data upon the power supply 306 being energized. For example, thepower supply 306 may be energized by a radio frequency or upon apressure-switch being activated and closing a circuit. In embodiments,the edge reporting module 312 may instruct a sensing component 302 tocapture sensor data in response to receiving a command to report sensordata from the edge device 104 or a human user (e.g., in response to theuser pressing a button).

In embodiments, a sensor 102 includes a housing (not shown). The sensorhousing may have any suitable form factor. In embodiments where thesensor 102 is being used outdoors, the sensor may have a housing that iswaterproof and/or resistant to extreme cold and/or extreme heat. Inembodiments, the housing may have suitable coupling mechanisms toremovably couple to an industrial component.

The foregoing is an example of a sensor 102. The sensor 102 may haveadditional or alternative components without departing from the scope ofthe disclosure.

FIG. 4 illustrates an example of an edge device 104. In embodiments, theedge device 104 may include a storage system 402, a communication system404, and a processing system 406. The edge device 104 may includeadditional components not shown, such as a power supply, a userinterface, and the like.

The storage system 402 includes one or more storage devices. The storagedevices may include persistent storage mediums (e.g., flash memorydrive, hard disk drive) and/or transient storage devices (e.g., RAM).The storage system 402 may store one or more data stores. A data storemay include one or more databases, tables, indexes, records,filesystems, folders and/or files. In the illustrated embodiments, thestorage device stores a configuration data store 410, a sensor datastore 412, and a model data store 414. A storage system 402 may storeadditional or alternative data stores without departing from the scopeof the disclosure.

In embodiments, the configuration data store 410 stores data relating tothe configuration of the sensor kit 100, including the devices of thesensor kit 100. In some embodiments, the configuration data store 410may maintain a set of device records. The device records may indicate adevice identifier that uniquely identifies a device of the sensor kit100. The device records may further indicate the type of device (e.g., asensor, a collection device, a gateway device, etc.). In embodimentswhere the network paths from each device to the edge device 104 do notchange, a device record may also indicate the network path of the deviceto the edge device 104 (e.g., any intermediate devices in the device'snetwork path). In the case that a device record corresponds to a sensor102, the device record may indicate the type of sensor (e.g., a sensortype identifier) and/or a type of data that is provided by the sensor102.

In embodiments, the configuration data store 410 may maintain a set ofsensor type records, where each record corresponds to a different typeof sensor 102 in the sensor kit 100. A sensor type record may indicate atype identifier that identifies the type of sensor and/or the type ofsensor data provided by the sensor. In embodiments, a sensor type recordmay further indicate relevant information relating to the sensor data,including maximum or minimum values of the sensor data, error codesoutput by sensors 102 of the sensor type, and the like.

In embodiments, the configuration data store 410 may maintain a map ofthe sensor kit network 200. The map of the sensor kit network 200 mayindicate a network topology of the sensor kit network 200, includingnetwork paths of the collection of devices in the sensor kit 100. Insome embodiments, the map may include physical locations of the sensorsas well. The physical location of a sensor 102 may be defined as a roomor area that the sensor 102 is in, a specific industrial component thatthe sensor 102 is monitoring, a set of coordinates relative of the edgedevice 104 (e.g., x, y, z coordinates relative to the edge device 104,or an angle and distance of the sensor 102 relative to the edge device104), an estimated longitude and latitude of the sensor 102, or anyother suitable format of relative or absolute location determinationand/or measurement.

In embodiments, a sensor data store stores 412 stores sensor datacollected from the sensors 102 of the sensor kit 100. In embodiments,the sensor data store 412 maintains sensor data that is collected over aperiod of time. In some of these embodiments, the sensor data store 412may be a cache that stores sensor data until it is reported and backedup at the backend system 150. In these embodiments, the cache may becleared when sensor data is reported to the backend system 150. In someembodiments, the sensor data store 412 stores all sensor data collectedby the sensor kit 412. In these embodiments, the sensor data store 412may provide a backup for all the sensor data collected by the sensor kit100 over time, thereby ensuring that the owner of the sensor kit 100maintains ownership of its data.

In embodiments, a model data store 414 stores machine-learned models.The machine-learned models may include any suitable type of models,including neural networks, deep neural networks, recursive neuralnetworks, Bayesian neural networks, regression-based models, decisiontrees, prediction trees, classification trees, Hidden Markov Models,and/or any other suitable types of models. A machine-learned model maybe trained on training data, which may be expert generated data,historical data, and/or outcome-based data. Outcome-based data may bedata that is collected after a prediction or classification is made thatindicates whether the prediction or classification was correct orincorrect and/or a realized outcome. A training data instance may referto a unit of training data that includes a set of features and a label.In embodiments, the label in a training data instance may indicate acondition of an industrial component or an industrial setting 120 at agiven time. Examples of conditions will vary greatly depending on theindustrial setting 120 and the conditions that the machine-learned modelis being trained to predict or classify. Examples of labels in amanufacturing facility may include, but are not limited to, no issuesdetected, a mechanical failure of a component, an electrical failure ofa component, a chemical leak detected, and the like. Examples of labelsin a mining facility may include, but are not limited to, no issuesdetected, an oxygen deficiency, the presence of a toxic gas, a failingstructural component, and the like. Examples of labels in an oil and/orgas facility (e.g., oil field, gas field, oil refinery, pipeline) mayinclude, but are not limited to, no issues detected, a mechanicalfailure of a component (e.g., a failed valve or failed O-ring), a leak,and the like. Examples of labels in an indoor agricultural facility mayinclude, but are not limited to, no issues detected, a plant died, aplant wilted, a plant turned a certain color (e.g., brown, purple,orange, or yellow), mold found, and the like. In each of these examples,there are certain features that may be relevant to a condition and somefeatures that may have little or no bearing on the condition. Through amachine-learning process (which may be performed at the backend system150 or another system), the model is trained to determine predictions orclassifications based on a set of features. Thus, the set of features ina training data instance may include sensor data that is temporallyproximate to a time when a condition of the industrial component orindustrial setting 120 occurred (e.g., the label associated with theindustrial component or industrial setting 120).

In embodiments, the machine-learned models may include prediction modelsthat are used to predict potential issues relating to an industrialcomponent being monitored. In some of these embodiments, amachine-learned model may be trained on training data (expert generateddata and/or historical data) that corresponds to one or more conditionsrelating to a particular component. In some of these embodiments, thetraining data sets may include sensor data corresponding to scenarioswhere maintenance or some intervening action was later required andsensor data corresponding to scenarios where maintenance or someintervening action was ultimately not required. In these exampleembodiments, the machine-learned model may be used to determine aprediction of one or more potential issues that may arise with respectto one or more industrial components being monitored and/or theindustrial setting 120 being monitored.

In embodiments, the machine-learned models may include classificationmodels that classify a condition of an industrial component beingmonitored and/or the industrial setting 120. In some of theseembodiments, a machine-learned model may be trained on training data(e.g., expert generated data and/or historical data) that corresponds toone or more conditions relating to a particular component. In some ofthese embodiments, the training data sets may include sensor datacorresponding to scenarios where respective industrial components and/orrespective industrial settings 120 were operating in a normal conditionand sensor data where the respective industrial components and/orrespective industrial settings 120 were operating in an abnormalcondition. In training data instances where there was an abnormalcondition, the training data instance may include a label indicating thetype of abnormal condition. For example, a training data instancecorresponding to an indoor agricultural facility that was deemed toohumid for ideal growing conditions may include a label that indicatesthe facility was too humid.

In embodiments, the communication system 404 includes two or morecommunication devices, including at least one internal communicationdevice that communicates with the sensor kit network 200 and at leastone external communication device that communicates with a publiccommunication network (e.g., the Internet) either directly or via agateway device. The at least one internal communication devices mayinclude Bluetooth chips, Zigbee chips, XBee chips, Wi-Fi chips, and thelike. The selection of the internal communication devices may depend onthe environment of the industrial setting 120 and the impacts thereof onthe sensors 102 to be installed therein (e.g., whether the sensors 102have reliable power sources, whether the sensors 102 will be spaced inproximity to one another, whether the sensors 102 need to transmitthrough walls, and the like). The external communication devices mayperform wired or wireless communication. In embodiments, the externalcommunication devices may include cellular chipsets (e.g., 4G or 5Gchipsets), Ethernet cards, satellite communication cards, or othersuitable communication devices. The external communication device(s) ofan edge device 104 may be selected based on the environment of theindustrial setting 120 (e.g., indoors v. outdoors, thick walls thatprevent wireless communication v. thin walls that allow wirelesscommunication, located near cellphone towers v. located in remote areas)and the preferences of an operator of the industrial setting 120 (e.g.,the operator allows the edge device 104 to access a private network ofthe industrial setting 120, or the operator does not allow the edgedevice 104 to access a private network of the industrial setting 120).

In embodiments, the processing system 406 may include one or more memorydevices (e.g., ROM and/or RAM) that store computer-executableinstructions and one or more processors that execute thecomputer-executable instructions. The processing system 406 may executeone or more of a data processing module 420, an encoding module 422, aquick-decision AI module 424, a notification module 426, a configurationmodule 428, and a distributed ledger module 430. The processing system406 may execute additional or alternative modules without departing fromthe scope of the disclosure. Furthermore, the modules discussed hereinmay include submodules that perform one or more functions of arespective module.

In embodiments, the data processing module 420 receives sensor data fromthe sensor kit network 200 and performs one or more data processingoperations on the received sensor data. In embodiments, the dataprocessing module 420 receives reporting packets 320 containing sensordata. In some of these embodiments, the data processing module 420 mayfilter data records that are duplicative (e.g., filtering out one out oftwo reporting packets 320 received from two respective sensorsmonitoring the same component for redundancy). The data processingmodule 420 may additionally or alternatively filter and/or flagreporting packets 320 containing sensor data that is clearly erroneous(e.g., sensor not within a tolerance range given the type of sensor 102or contains an error code). In embodiments, the data processing module420 may store and/or index the sensor data in the sensor data store.

In embodiments, the data processing module 420 may aggregate sensor datareceived over a period of time from the sensors 102 of the sensor kit100 or a subset thereof and may transmit the sensor data to the backendsystem 150. In transmitting sensor data to the backend system 150, thedata processing module 420 may generate a sensor kit reporting packetthat includes one or more instances of sensor data. The sensor data inthe sensor kit reporting packet may be compressed or uncompressed. Inembodiments, the sensor kit reporting packet may indicate a sensor kitidentifier that identifies the source of the data packet to the backendsystem 150. In embodiments, the data processing module 420 may transmitthe sensor data upon receipt of the sensor data from a sensor 102, atpredetermined intervals (e.g., every second, every minute, every hour,every day), or in response to a triggering condition (e.g., a predictionor classification that there is an issue with an industrial component orthe industrial setting 120 based on received sensor data). In someembodiments, the sensor data may be encoded/compressed, such that sensordata collected from multiple sensors 102 and/or over a period of timemay be more efficiently transmitted. In embodiments, the data processingmodule 420 may leverage the quick-decision AI module 424 to determinewhether the industrial components of the industrial setting 120 and/orthe industrial setting 120 itself is likely in a normal condition. Ifthe quick-decision AI module 424 determines that the industrialcomponents and/or the industrial setting 120 are in a normal conditionwith a high degree of certainty, then the data processing module 420 maydelay or forgo transmitting the sensor data used to make theclassification to the backend system 150. Additionally or alternatively,if the quick-decision AI module 424 determines that the industrialcomponents and/or the industrial setting 120 are in a normal conditionwith a high degree of certainty, then the data processing module 420 maycompress the sensor data and may be compressed at a greater rate. Thedata processing module 420 may perform additional or alternativefunctions without departing from the scope of the disclosure.

In embodiments, the encoding module 422 receives sensor data and mayencode, compress, and/or encrypt the sensor data. The encoding module422 may employ other techniques to compress the sensor data. Inembodiments, the encoding module 422 may employ horizontal orcompression techniques to compress the sensor data. For example, theencoding module 422 may use the Lempel-Zev-Welch algorithm or variationsthereof. In some embodiments, the encoding module 522 may representsensor data in an original integer or “counts format” and with relevantcalibration coefficients and offsets at the time of collection. In theseembodiments, the coefficients and offsets may be coalesced at the timeof collection when a precise signal path is known, such that onefloating-point coefficient and one integer offset is stored for eachchannel.

In embodiments, the encoding module 422 may employ one or more codecs tocompress the sensor data. The codecs may be proprietary codecs and/orpublicly available codecs. In some embodiments, the encoding module 422may use a media compression codec (e.g., a video compression codec) tocompress the sensor data. For example, the encoding module 422 maynormalize the sensor data into values that fall within a range andformat of a media frame (e.g., normalizing sensor data into acceptablepixel values for inclusion into a video frame) and may embed thenormalized sensor data into the media frame. The encoding module 422 mayembed the normalized sensor data collected from the sensors 102 of thesensor kit 100 into the media frame according to a predefined mapping(e.g., a mapping of respective sensors 102 to one or more respectivepixels in a media frame). The encoding module 422 may generate a set ofconsecutive media frames in this manner and may compress the mediaframes using a media codec (an H.264/MPEG-4 codec, an H.265/MPEG-Hcodec, an H.263/MPEG-4 codec, proprietary codecs, and the like) toobtain a sensor data encoding. The encoding module 422 may then transmitsensor data encoding to the backend system, which may decompress andrecalculate the sensor data based on the normalized values. In theseembodiments, the codec used for compression and the mappings of sensorsto pixels may be selected to reduce lossiness or to increase compressionrates. Furthermore, the foregoing technique may be applied to sensordata that tends to be more static and less changing between samplingsand/or where sensor data collected from different sensors tend to havelittle variation when sampled at the same time. The encoding module 422may employ additional or alternative encoding/compression techniqueswithout departing from the scope of the disclosure.

In embodiments, the quick-decision AI module 424 may utilize a limitedset of machine-learned models to generate predictions and/orclassifications of a condition of an industrial component beingmonitored and/or of the industrial setting 120 being monitored. Inembodiments, the quick-decision AI module 424 may receive a set offeatures (e.g., one or more sensor data values) and request for aspecific type of prediction or classification based thereon. Inembodiments, the quick-decision AI module 424 may leverage amachine-learned model corresponding to the requested prediction orclassification. The quick-decision AI module 424 may generate a featurevector based on the received features, such that the feature vectorincludes one or more sensor data values obtained from one or moresensors 102 of the sensor kit 100. The quick-decision AI module 424 mayfeed the feature vector to the machine-learned model. Themachine-learned model may output a prediction or classification and adegree of confidence in the prediction or classification. Inembodiments, the quick-decision AI module 424 may output the predictionor classification to the data processing module 420 (or another modulethat requested a prediction or classification). For example, inembodiments the data processing module 420 may use classifications thatthe industrial components and/or the industrial setting 120 are in anormal condition to delay or forgo transmission of sensor data and/or tocompress sensor data. In embodiments, the data processing module 420 mayuse a prediction or classification that the industrial components and/orthe industrial setting 120 are likely to encounter a malfunction totransmit uncompressed sensor data to the backend system 150, which mayfurther analyze the sensor data and/or notify a human user of apotential issue.

In embodiments, the notification module 426 may provide notifications oralarms to users based on the sensor data. In some of these embodiments,the notification module 426 may apply a set of rules that trigger anotification or alarm if certain conditions are met. The conditions maydefine sensor data values that are strongly correlated with anundesirable (e.g., emergency) condition. Upon receiving sensor data fromthe data processing module 420, the notification module 426 may applyone or more rules to the sensor data. If the conditions to trigger analarm or notification are met, the notification module 426 may issue analarm or notification to a human user. The manner by which an alarm ornotification is provided to the human user (e.g., to a user device, ortriggering an audible alarm) may be predefined or, in some embodiments,may be defined by an operator of the industrial setting 120.

In embodiments, the configuration module 428 configures the sensor kitnetwork 200. In embodiments, the configuration module 428 may transmitconfiguration requests to the other devices in the sensor kit 100, uponthe sensors 102, edge device 104, and any other devices being installedin the industrial setting 120. In some of these embodiments, the sensors102 and/or other devices may establish a mesh network or a hierarchicalnetwork in response to the configuration requests. In embodiments, thesensors 102 and other devices in the sensor kit network may respond tothe configuration requests, in response to the configuration requests.In embodiments, the configuration module 428 may generate device recordscorresponding to the devices that responded based on the device IDs ofthose devices and any additional data provided in the responses to theconfiguration requests.

In embodiments, the configuration module 428 adds new devices to thesensor kit 100. In these embodiments, the configuration module 428 addsnew sensors 102 to the sensor kit 100 post-installation in aplug-and-play-like manner. In some of these embodiments, thecommunication devices 404, 308 of the edge device 104 and the sensors102 (or other devices to be added to the sensor kit 100) may includerespective short-range communication capabilities (e.g., near-fieldcommunication (NFC) chips). In these embodiments, the sensors 102 mayinclude persistent storage that stores identifying data (e.g., a sensorid value) and any other data that would be used to add the sensor to thesensor kit (e.g., device type, supported communication protocols, andthe like). In response to a user initiating a post-installation additionto the sensor kit 100 (e.g., the user pressing a button on the edgedevice 104 and/or bringing the sensor 102 into the vicinity of the edgedevice 104), the configuration module 428 may cause the communicationsystem 404 to emit a signal (e.g., a radio frequency). The emittedsignal may trigger a sensor 102 proximate enough to receive the signalto transmit its sensor ID and any other suitable configuration data(e.g., device type, communication protocols, and the like). In responseto the sensor 102 transmitting its configuration data (sensor ID andother relevant configuration data) to the edge device 104, theconfiguration module 428 may add the new sensor 102 to the sensor kit102. In embodiments, adding the sensor 102 to the sensor kit 104 mayinclude generating a new device record corresponding to the new sensor102 based on the sensor id updating the configuration data store 410with the new device record. The configuration module 428 may add a newsensor 102 to the sensor kit 100 in any other suitable manner.

In embodiments, the edge device 104 may include a distributed ledgermodule 430. In embodiments, the distributed ledger module 430 may beconfigured to update a distributed ledger 162 with sensor data capturedby the sensor kit 100. In embodiments, the distributed ledger may bedistributed amongst a plurality of node computing devices 160. Asdiscussed, in embodiments, a distributed ledger 162 is comprised of aset of linked data structures (e.g., blocks, data records, etc.). Forpurposes of explanation, the data structures will be referred to asblocks.

As discussed, each block may include a header that includes a unique IDof the block and a body that includes the data that is stored in theblock and a pointer of a parent block. In embodiments, the pointer inthe block is the block ID of a parent block of the block. The datastored in a respective block can be sensor data captured by a respectivesensor kit 100. Depending on the implementation, the types of sensordata and the amount of sensor data stored in a respective body of ablock may vary. For example, a block may store a set of sensormeasurements from one or more types of sensors 102 in the sensor kit 100captured over a period of time (e.g., sensor data 102 captured from allof the sensors 102 in the sensor kit 100 over a period one hour or oneday) and metadata relating thereto (e.g., sensor IDs of each sensormeasurement and a timestamp of each sensor measurement or group ofsensor measurements). In some embodiments, a block may store sensormeasurements determined to be anomalous (e.g., outside a standarddeviation of expected sensor measurements or deltas in sensormeasurements that are above a threshold) and/or sensor measurementsindicative of an issue or potential issue, and related metadata (e.g.,sensor IDs of each sensor measurement and a timestamp of each sensormeasurement or group of sensor measurements). In some embodiments, thesensor data stored in a block may be compressed and/or encoded sensordata, such that the encoding module 422 compresses/encodes the sensordata into a more compact format. In embodiments, the distributed ledgermodule 430 may generate a hash of the body, such that the contents ofthe body (e.g., block ID of the parent block and the sensor data) arehashed and cannot be altered without changing the value of the hash. Inembodiments, the distributed ledger module 430 may encrypt the contentwithin the block, so that the content may not be read by unauthorizeddevices.

In embodiments, the distributed ledger module 430 generates a block inresponse to a triggering event. Examples of triggering events mayinclude a predetermined time (e.g., every minute, every hour, everyday), when a potential issue is classified or predicted, when one ormore sensor measurements are outside of a tolerance threshold, or thelike. In response to the triggering event, the distributed ledger module430 may generate a block based on sensor data that is to be reported.Depending on the configuration of the server kit 100 and the intendeduse of the distributed ledger 162, the amount of data and type of datathat is included in a block may vary. For example, in a manufacturing orresource extraction setting such as the manufacturing facility 1700 orthe underwater industrial setting 1800, the distributed ledger 162 maybe used to demonstrate functional machinery and/or to predictmaintenance needs. In this example, the distributed ledger module 430may be accessible by insurance providers to set insurance rates and/orissue refunds. Thus, in this example, the distributed ledger module 430may include any sensor measurements (and related metadata) that areoutside of a tolerance threshold or instance where an issue isclassified or predicted. In another example, the distributed ledger maybe accessible by a regulatory body to ensure that a facility isoperating in accordance with one or more regulations. In theseembodiments, the distributed ledger module 430 may store a set of one ormore sensor measurements (and related metadata) in a block, such thatthe sensor measurements may be analyzed by the regulatory agency. Insome of these embodiments, the sensor measurements may be compressed tostore more sensor data in a single block. In response to generating ablock, the distributed ledger module 430 may transmit the block to oneor more node computing devices 160. Upon the block being verified (e.g.,using a consensus mechanism), each node computing device 160 may updatethe distributed ledger 162 with the new block.

As discussed, in some embodiments the distributed ledger may furtherinclude smart contracts. Once written, a smart contract may be encodedin a block and deployed to the distributed ledger 162. The address ofthe smart contract (e.g., the block ID of the block containing the smartcontract) may be provided to one or more parties to the smart contract,such that respective parties may invoke the smart contract using theaddress. In some of these embodiments, the address of the smart contractmay be provided to the distributed ledger module 430, such that thedistributed ledger module 430 may report items to the smart contract. Insome embodiments, the distributed ledger module 430 may leverage the APIof a smart contract to report the items to the smart contract.

In example implementations discussed above, an insurer may utilize asmart contract to allow insured facility owners and/or operators todemonstrate that the equipment in the facility is functioning properly.In some embodiments, the smart contract may trigger the issuance ofrebates or refunds on portions of insurance premiums when an ownerand/or operator of a facility provides sufficient sensor data thatindicates the facility is operating without issue. In some of theseembodiments, the smart contract may include a first condition thatrequires a certain amount of sensor data to be reported by a facilityand a second condition that each instance of the sensor data equals avalue (e.g., no classified or predicted issues) or range of values(e.g., all sensor measurements within a predefined range of values). Insome embodiments, the action may be to deposit funds (e.g., a wiretransfer or cryptocurrency) into an account in response to the first andsecond conditions being met. In this example, the distributed ledgermodule 430 may write blocks containing sensor data to the distributedledger 162. The distributed ledger module 430 may also provide theaddresses of these blocks to the smart contract (e.g., via an API of thesmart contract). Upon the smart contract verifying the first and secondconditions of the contract, the smart contract may initiate the transferof funds from an account of the insurer to the account of the insured.

In another example discussed above, a regulatory body (e.g., a state,local, or federal regulatory agency) may utilize a smart contract thatmonitors facilities (e.g., food inspection facilities, pharmaceuticalmanufacturing facilities, indoor agricultural facilities, offshore oilextraction facilities, or the like) based on reported sensor data toensure compliance with one or more regulations. In embodiments, thesmart contract may be configured to receive and verify the sensor datafrom a facility (e.g., via an API of the smart contract), and inresponse to verifying the sensor data issues a compliance token (orcertificate) to an account of the facility owner. In some of theseembodiments, the smart contract may include a first condition thatrequires a certain amount of sensor data to be reported by a facilityand a second condition that requires the sensor data to be compliantwith the reporting regulations. In this example, the distributed ledgermodule 430 may write blocks containing sensor data to the distributedledger. The sensor kit 100 may also provide the addresses of theseblocks to the smart contract (e.g., using an API of the smart contract).Upon the smart contract verifying the first and second conditions of thecontract, the smart contract may generate a token indicating complianceby the facility operator, and may initiate the transfer of funds to anaccount (e.g., a digital wallet) associated with the facility.

FIG. 5 illustrates an example backend system 150 according to someembodiments of the present disclosure. In embodiments, the backendsystem 150 may be implemented as a cloud service that is executed at oneor more physical server devices. In embodiments, the backend system 150may include a storage system 502, a communication system 504, and aprocessing system 506. The backend system 150 may include additionalcomponents not shown.

A storage system 502 includes one or more storage devices. The storagedevices may include persistent storage mediums (e.g., flash memorydrive, hard disk drive) and/or transient storage devices (e.g., RAM).The storage system 502 may store one or more data stores. A data storemay include one or more databases, tables, indexes, records,filesystems, folders and/or files. In the illustrated embodiments, thestorage system 502 stores a sensor kit data store 510 and a model datastore 512. A storage system 502 may store additional or alternative datastores without departing from the scope of the disclosure.

In embodiments, the sensor kit data store 510 stores data relating torespective sensor kits 100. In embodiments, the sensor kit data store510 may store sensor kit data corresponding to each installed sensor kit100. In embodiments, the sensor kit data may indicate the devices in asensor kit 100, including each sensor 102 (e.g., a sensor ID) in thesensor kit 100. In some embodiments, the sensor kit data may indicatethe sensor data captured by the sensor kit 100. In some of theseembodiments, the sensor kit data may identify each instance of sensordata captured by the sensor kit 100, and for each instance of sensordata, the sensor kit data may indicate the sensor 102 that captured thesensor data and, in some embodiments, a time stamp corresponding to thesensor data.

In embodiments, the model data store 512 stores machine-learned modelsthat are trained by the AI system 524 based on training data. Themachine-learned models may include prediction models and classificationmodels. In embodiments, the training data used to train a particularmodel includes data collected from one or more sensor kits 100 thatmonitor the same type of industrial setting 120. The training data mayadditionally or alternatively may include historical data and/or expertgenerated data. In embodiments, each machine-learned model may pertainto a respective type of industrial setting 120. In some of theseembodiments, the AI system 524 may periodically update a machine-learnedmodel pertaining to a type of industrial setting 120 based on sensordata collected from sensor kits 100 monitoring those types of industrialsetting 120 and outcomes obtained from those industrial setting 120. Inembodiments, machine-learned models pertaining to a type of industrialsetting 120 may be provided to the edge devices 104 of sensor kits 100monitoring that type of industrial setting 120.

In embodiments, a communication system 504 includes one or morecommunication devices, including at least one external communicationdevice that communicates with a public communication network (e.g., theInternet) ether. The external communication devices may perform wired orwireless communication. In embodiments, the external communicationdevices may include cellular chipsets (e.g., 4G or 5G chipsets),Ethernet cards and/or Wi-Fi cards, or other suitable communicationdevices.

In embodiments, the processing system 506 may include one or more memorydevices (e.g., ROM and/or RAM) that store computer-executableinstructions and one or more processors that execute thecomputer-executable instructions. The processors may execute in aparallel or distributed manner. The processors may be located in thesame physical server device or in different server devices. Theprocessing system 506 may execute one or more of a decoding module 520,a data processing module 522, an AI module 524, a notification module526, an analytics module 528, a control module 530, a dashboard module532, a configuration module 534, and a distributed ledger managementmodule 536. The processing system 406 may execute additional oralternative modules without departing from the scope of the disclosure.Furthermore, the modules discussed herein may include submodules thatperform one or more functions of a respective module.

In embodiments, a sensor kit 100 may transmit encoded sensor kit packetscontaining sensor data to the backend system 150. In these embodiments,the decoding module 520 may receive encoded sensor data from an edgedevice 104 and may decrypt, decode, and/or decompress the encoded sensorkit packets to obtain the sensor data and metadata relating to thereceived sensor data (e.g., a sensor kit id and one or more sensor idsof sensors that captured the sensor data). The decoding module 520 mayoutput the sensor data and any other metadata to the data processingmodule 522.

In embodiments, the data processing module 522 may process the sensordata received from the sensor kits 100. In some embodiments, the dataprocessing module 522 may receive the sensor data and may store thesensor data in the sensor kit data store 510 in relation to the sensorkit 100 that provided to the sensor data. In embodiments, the dataprocessing system 522 may provide AI-related requests to the AI module524. In these embodiments, the data processing system 522 may extractrelevant sensor data instances from the received sensor data and mayprovide the extracted sensor data instances to the AI module 524 in arequest that indicates the type of request (e.g., what type ofprediction or classification) and the sensor data to be used. In theevent a potential issue is predicted or classified, the data processingmodule 522 may execute a workflow associated with the potential issue. Aworkflow may define the manner by which a potential issue is handled.For instance, the workflow may indicate that a notification should betransmitted to a human user, a remedial action should be initiated,and/or other suitable actions. The data processing module 522 mayperform additional or alternative processing tasks without departingfrom the scope of the disclosure.

In embodiments, the AI module 524 trains machine-learned models that areused to make predictions or classifications. The machine-learned modelsmay include any suitable type of models, including neural networks, deepneural networks, recursive neural networks, Bayesian neural networks,regression-based models, decision trees, prediction trees,classification trees, Hidden Markov Models, and/or any other suitabletypes of models. The AI module 524 may train a machine-learned model ona training data set. A training data set may include expert-generateddata, historical data, and/or outcome-based data. Outcome-based data maybe data that is collected after a prediction or classification is madethat indicates whether the prediction or classification was correct orincorrect and/or a realized outcome. A training data instance may referto a unit of training data that includes a set of features and a label.In embodiments, the label in a training data instance may indicate acondition of an industrial component or an industrial setting 120 at agiven time. Examples of conditions will vary greatly depending on theindustrial setting 120 and the conditions that the machine-learningmodel is being trained to predict or classify. Examples of labels in amanufacturing facility may include, but are not limited to, no issuesdetected, a mechanical failure of a component, an electrical failure ofa component, a chemical leak detected, and the like. Examples of labelsin a mining facility may include, but are not limited to, no issuesdetected, an oxygen deficiency, the presence of a toxic gas, a failingstructural component, and the like. Examples of labels in an oil and/orgas facility (e.g., oil field, gas field, oil refinery, pipeline) mayinclude, but are not limited to, no issues detected, a mechanicalfailure of a component (e.g., a failed valve or failed O-ring), a leak,and the like. Examples of labels in an indoor agricultural facility mayinclude, but are not limited to, no issues detected, a plant died, aplant wilted, a plant turned a certain color (e.g., brown, purple,orange, or yellow), mold found, and the like. In each of these examples,there are certain features that may be relevant to a condition and somefeatures that may have little or no bearing on the condition. Inembodiments, the AI module 524 may reinforce the machine-learned modelsas more sensor data and outcomes relating to the machine-learned modelsare received. In embodiments, the machine-learned models may be storedin the model data store 512. Each model may be stored with a modelidentifier, which may be indicative of (e.g., mapped to) the type ofindustrial setting 120 that the model makes, the type of prediction orclassification made by the model, and the features that the modelreceives. In some embodiments, one or more machine-learned models (andsubsequent updates thereto) may be pushed to respective sensor kits 100,whereby the edge devices 104 of the respective sensor kits 100 may useone or more machine-learned model to make predictions and/orclassifications without having to rely on the backend system 150.

In embodiments, the AI module 524 receives requests for predictionsand/or classifications and determines predictions and/or classificationsbased on the requests. In embodiments, a request may indicate a type ofprediction or classification that is being requested and may include aset of features for making the prediction or classification. In responseto the request, the AI module 524 may select a machine-learned model toleverage based on the type of prediction or classification beingrequested, whereby the selected model receives a certain set offeatures. The AI module 524 may then generate a feature vector thatincludes one or more instances of sensor data and may feed the featurevector into the selected model. In response to the feature vector, theselected model may output a prediction or classification, and a degreeof confidence (e.g., a confidence score) in the prediction orclassification. The AI module 524 may output the prediction orclassification, as well as the degree of confidence therein, to themodule that provided the request.

In embodiments, the notification module 526 may issue notifications tousers and/or respective industrial setting 120 when an issue is detectedin a respective setting. In embodiments, a notification may be sent to auser device of a user indicating the nature of the issue. Thenotification module 526 may implement an API (e.g., a REST API), wherebya user device of a user associated with the industrial setting 120 mayrequest notifications from the backend system 150. In response to therequest, the notification module 526 may provide any notifications, ifany, to the user device. In embodiments, a notification may be sent to adevice located at an industrial setting 120, whereby the device mayraise an alarm at the industrial setting 120 in response to theindustrial setting 120.

In embodiments, the analytics module 528 may perform analytics relatedtasks on sensor data collected by the backend system 150 and stored inthe sensor kit data store 510. In embodiments, the analytics tasks maybe performed on sensor data received from individual sensor kits.Additionally, or alternatively, the analytics tasks may be performed onsensor data Examples of analytics tasks that may be performed on sensordata obtained from various sensor kits 100 monitoring differentindustrial setting 120. Examples of analytics tasks may include energyutilization analytics, quality analytics, process optimizationanalytics, financial analytics, predictive analytics, yield optimizationanalytics, fault prediction analytics, scenario planning analytics, andmany others.

In embodiments, the control module 530 may control one or more aspectsof an industrial setting 120 based on a determination made by the AIsystem 524. In embodiments, the control module 530 may be configured toprovide commands to a device or system at the industrial setting 120 totake a remedial action in response to a particular issue being detected.For example, the control module 530 may issue a command to amanufacturing facility to stop an assembly line in response to adetermination that a critical component on the assembly line is likelyfailing or likely failed. In another example, the control module 530 mayissue a command to an agricultural facility to activate a dehumidifierin response to a determination that the humidity levels are too high inthe facility. In another example, the control module 530 may issue acommand to shut a valve in an oil pipeline in response to adetermination that a component in the oil pipeline downstream to thevalve is likely failing or likely failed. For a particular industrialsetting 120, the control module 530 may perform remedial actions definedby a human user associated with the industrial setting 120, such thatthe human user may define what conditions may trigger the remedialaction.

In embodiments, the dashboard module 532 presents a dashboard to humanusers via a user device 140 associated with the human user. Inembodiments, the dashboard provides a graphical user interface thatallows the human user to view relating to a sensor kit 100 with whichthe human user is associated (e.g., an employee at the industrialsetting 120). In these embodiments, the dashboard module 532 mayretrieve and display raw sensor data provided by the sensor kit,analytical data relating to the sensor data provided by the sensor kit100, predictions or classifications made by the backend system 150 basedon the sensor data, and the like.

In embodiments, the dashboard module 532 allows human users to configureaspects of the sensor kits 100. In embodiments, the dashboard module 532may present a graphical user interface that allows a human user toconfigure one or more aspects of a sensor kit 100 with which the humanuser is associated. In embodiments, the dashboard may allow a user toconfigure alarm limits with respect to one or more sensor types and/orconditions. For example, a user may define a temperature value at whicha notification is sent to a human user. In another example, the user maydefine a set of conditions, which if predicted by the AI module and/orthe edge device, trigger an alarm. In embodiments, the dashboard mayallow a user to define which users receive a notification when an alarmis triggered. In embodiments, the dashboard may allow a user tosubscribe to additional features of the backend system 150 and/or anedge device 104.

In embodiments, the dashboard may allow a user to add one or moresubscriptions to a sensor kit 100. The subscriptions may include accessto backend services and/or edge services. A user may select a service toadd to a sensor kit 100 and may provide payment information to pay forthe services. Upon verification of the payment information, the backendsystem 150 may provide the sensor kit 100 access to those features.Examples of services that may be subscribed to include analyticsservices, AI-services, notification services, and the like. Thedashboard may allow the user to perform additional or alternativeconfigurations.

In embodiments, the configuration module 534 maintains configurations ofrespective sensor kits 100. Initially, when a new sensor kit 100 isdeployed in an industrial setting 120, the configuration module 534 mayupdate the sensor kit data store 510 with the device IDs of each devicein the newly installed sensor kit 100. Once the sensor kit data store510 has updated the sensor kit data store 510 to reflect the newlyinstalled sensor kit 100, the backend system 150 may begin storingsensor data from the sensor kit 100. In embodiments, new sensors 102 maybe added to respective sensor kits 100. In these embodiments, an edgedevice 104 may provide an add request to the backend system 150 upon anattempt to add a device to the sensor kit 100. In embodiments, therequest may indicate a sensor ID of the new sensor. In response to therequest, the configuration module 534 may add the sensor ID of the newsensor to the sensor kit data of the requesting sensor kit 100 in thesensor kit data store 510.

In embodiments, the backend system 150 includes a distributed ledgermanagement module 536. In some of these embodiments, the distributedledger management module 536 allows a user to update and/or configure adistributed ledger. In some of these embodiments, the distributed ledgermanagement module 536 allows a user to define or upload a smartcontract. As discussed, the smart contract may include one or moreconditions that are verified by the smart contract and one or moreactions that are triggered when the conditions are verified. Inembodiments, the user may provide one or more conditions that are to beverified to the distributed ledger management module 536 via a userinterface. In some of these embodiments, the user may provide the code(e.g., JavaScript code, Java code, C code, C++ code, etc.) that definesthe conditions. The user may also provide the actions that are to beperformed in response to certain conditions being met. In response to asmart contract being uploaded/created, the distributed ledger managementmodule 536 may deploy the smart contract. In embodiments, thedistributed ledger management module 536 may generate a block containingthe smart contract. The block may include a header that defines anaddress of the block, and a body that includes an address to a previousblock and the smart contract. In some embodiments, the distributedledger management module 536 may determine a hash value based on thebody of the block and/or may encrypt the block. The distributed ledgermanagement module 536 may transmit the block to one or more nodecomputing devices 160, which in turn update the distributed ledger withthe block containing the smart contract. The distributed ledgermanagement module 536 may further provide the address of the block toone or more parties that may access the smart contract. The distributedledger management module 536 may perform additional or alternativefunctions without departing from the scope of the disclosure.

The backend system 150 may include additional or alternative components,data stores, and/or modules that are not discussed.

FIGS. 6-9—Exemplary Methods of Encoding and/or Decoding Sensor Data

FIG. 6 illustrates an example set of operations of a method 600 forcompressing sensor data obtained by a sensor kit 100. In embodiments,the method 600 may be performed by an edge device 104 of a sensor kit100.

At 610, the edge device 104 receives sensor data from one or moresensors 102 of the sensor kit 100 via a sensor kit network 200. Inembodiments, the sensor data from a respective sensor 102 may bereceived in a reporting packet. Each reporting packet may include adevice identifier of the sensor 102 that generated the reporting packetand one or more instances of sensor data captured by sensor 102. Thereporting packet may include additional data, such as a timestamp orother metadata.

At 612, the edge device 104 processes the sensor data. In embodiments,the edge device 104 may dedupe any reporting packets that areduplicative. In embodiments, the edge device 104 may filter out sensordata that is clearly erroneous (e.g., outside of a tolerance range). Inembodiments, the edge device 104 may aggregate the sensor data obtainedfrom multiple sensors 102. In embodiments, the edge device 104 mayperform one or more AI related tasks, such as determining a predictionor classification relating to a condition of one or more industrialcomponents of the industrial setting 120. In some of these embodiments,the decision to compress the sensor data may depend on whether the edgedevice 104 determines that there are any potential issues with theindustrial component. For example, the edge device 104 may compress thesensor data when there have been no issues predicted or classified. Inother embodiments, the edge device 104 may compress any sensor data thatis being transmitted to the backend system or certain types of sensordata (e.g., sensor data obtained from temperature sensors).

At 614, the edge device 104 may compress the sensor data. The edgedevice 104 may employ any suitable compression techniques forcompressing the sensor data. For example, the edge device 104 may employvertical or horizontal compression techniques. The edge device 104 maybe configured with a codec that compresses the sensor data. The codecmay be a proprietary codec or an “off-the-shelf” codec.

At 616, the edge device 104 may transmit the compressed sensor data tothe backend system 150. In embodiments, the edge device 104 may generatea sensor kit packet that contains the compressed data. The sensor kitpacket may designate the source of the sensor kit packet (e.g., a sensorkit ID or edge device ID) and may include additional metadata (e.g., atimestamp). In embodiments, the edge device 104 may encrypt the sensorkit packet prior to transmitting the sensor kit packet to the backendsystem 150. In embodiments, the edge device 104 transmits the sensor kitpacket to the backend system 150 directly (e.g., via a cellularconnection, a network connection, or a satellite uplink). In otherembodiments, the edge device 104 transmits the sensor kit packet to thebackend system 150 via a gateway device, which transmits the sensor kitpacket to the backend system 150 directly (e.g., via a cellularconnection or a satellite uplink).

FIG. 7 illustrates an example set of operations of a method 700 forprocessing compressed sensor data received from a sensor kit 100. Inembodiments, the method 700 is executed by a backend system 150.

At 710, the backend system 150 receives compressed sensor data from asensor kit. In embodiments, the compressed sensor data may be receivedin a sensor kit packet.

At 712, the backend system 150 decompresses the received sensor data. Inembodiments, the backend system may utilize a codec to decompress thereceived sensor data. Prior to decompressing the received sensor data,the backend system 150 may decrypt a sensor kit packet containing thecompressed sensor data.

At 714, the backend system 150 performs one or more backend operationson the decompressed sensor data. The backend operations may includestoring the data, filtering the data, performing AI-related tasks on thesensor data, issuing one or more notifications in relation to theresults of the AI-related tasks, performing one or more analyticsrelated tasks, controlling an industrial component of the industrialsetting 120, and the like.

FIG. 8 illustrates an example set of operations of a method 800 forstreaming sensor data from a sensor kit 100 to a backend system 150. Inembodiments, the method 800 may be executed by an edge device 104 of thesensor kit 100.

At 810, the edge device 104 receives sensor data from one or moresensors 102 of the sensor kit 100 via a sensor kit network 200. Inembodiments, the sensor data from a respective sensor 102 may bereceived in a reporting packet. Each reporting packet may include adevice identifier of the sensor 102 that generated the reporting packetand one or more instances of sensor data captured by sensor 102. Thereporting packet may include additional data, such as a timestamp orother metadata. In embodiments, the edge device 104 may process thesensor data. For example, the edge device 104 may dedupe any reportingpackets that are duplicative and/or may filter out sensor data that isclearly erroneous (e.g., outside of a tolerance range). In embodiments,the edge device 104 may aggregate the sensor data obtained from multiplesensors 102.

At 812, the edge device 104 may normalize and/or transform the sensordata into a media-frame compliant format. In embodiments, the edgedevice 104 may normalize and/or transform each sensor data instance intoa value that adheres to the restrictions of a media frame that willcontain the sensor data. For example, in embodiments where the mediaframes are video frames, the edge device 104 may normalize and/ortransform instances of sensor data into acceptable pixel frames. Theedge device 104 may employ one or more mappings and/or normalizationfunctions to transform and/or normalize the sensor data.

At 814, the edge device 104 may generate a block of media frames basedon the transformed and/or normalized sensor data. For example, inembodiments where the media frames are video frames, the edge device 104may populate each instance of transformed and/or normalized sensor datainto a respective pixel of the video frame. The manner by which the edgedevice 104 assigns an instance of transformed and/or normalized sensordata to a respective pixel may be defined in a mapping that mapsrespective sensors to respective pixel values. In embodiments, themapping may be defined so as to minimize variance between the values inadjacent pixels. In embodiments, the edge device 104 may generate aseries of time-sequenced media frames, such that each successive mediaframe corresponds to a subsequent set of sensor data instances.

At 816, the edge device 104 may encode the block of the media frame. Inembodiments, the edge device 104 may employ an encoder of a media codec(e.g., a video codec) to compress the block of media frames. The codecmay be a proprietary codec or an “off-the-shelf” codec. For example, themedia codec may be an H.264/MPEG-4 codec, an H.265/MPEG-H codec, anH.263/MPEG-4 codec, proprietary codecs, and the like. The codec receivesthe block of media frames and generates an encoded media block basedthereon.

At 818, the edge device 104 may transmit the encoded media block to thebackend system 150. In embodiments, the edge device 104 may stream theencoded media blocks to the backend system 150. Each encoded block maydesignate the source of the block (e.g., a sensor kit ID or edge deviceID) and may include additional metadata (e.g., a timestamp and/or ablock identifier). In embodiments, the edge device 104 may encrypt theencoded media blocks prior to transmitting encoded media blocks to thebackend system 150. The edge device 104 may transmit the encoded mediablocks to the backend system 150 directly (e.g., via a cellularconnection, a network connection, or a satellite uplink) or via agateway device, which transmits the encoded media block to the backendsystem 150 directly (e.g., via a cellular connection or a satelliteuplink).

The edge device 104 may continue to execute the foregoing method 800, soas to deliver a stream of live sensor data from a sensor kit. Theforegoing method 900 may be performed in settings where there are manysensors deployed within the setting and the sensors are sampledfrequently or continuously. In this way, the bandwidth required toprovide the sensor data to the backend system is reduced.

FIG. 9 illustrates an example set of operations of a method 900 foringesting a sensor data stream from an edge device 104. In embodiments,the method 900 is executed by a backend system.

At 910, the backend system 150 receives an encoded media block from asensor kit. The backend system 150 may receive encoded media blocks aspart of a sensor data stream.

At 912, the backend system 150 decodes the encoded block using a decodercorresponding to the codec of the codec used to encode the media blockto obtain a set of successive media frames. As discussed with respect tothe encoding operation, the codec may be a proprietary codec or an“off-the-shelf” codec. For example, the media codec may be anH.264/MPEG-4 codec, an H.265/MPEG-H codec, an H.263/MPEG-4 codec,proprietary codecs, and the like. The codec receives the encoded blockof media frames and decodes the encoded block to obtain a set ofsequential media frames.

At 914, the backend system 150 recreates the sensor data based on themedia frame. In embodiments, the backend system 150 determines thenormalized and/or transformed sensor values embedded in each respectivemedia frame. For example, in embodiments where the media frames arevideo frames, the backend system 150 may determine pixel values for eachpixel in the media frame. A pixel value may correspond to respectivesensor 102 of a sensor kit 100 and the value may represent a normalizedand/transformed instance of sensor data. In embodiments, the backendsystem 150 may recreate the sensor data by inversing the normalizationand/or transformation of the pixel value. In embodiments, the backendsystem 150 may utilize an inverse transformation and/or an inversenormalization function to obtain each recreated sensor data instance.

AT 918, the backend system 150 performs one or more backend operationsbased on the recreated sensor data. The backend operations may includestoring the data, filtering the data, performing AI-related tasks on thesensor data, issuing one or more notifications in relation to theresults of the AI-related tasks, performing one or more analyticsrelated tasks, controlling an industrial component of the industrialsetting 120, and the like.

FIG. 10—Exemplary Method of Determining Transmission Strategy

FIG. 10 illustrates a set of operations of a method 1000 for determininga transmission strategy and/or a storage strategy for sensor datacollected by a sensor kit 100 based on the sensor data. A transmissionstrategy may define a manner that sensor data is transmitted (if at all)to the backend system. For example, sensor data may be compressed usingan aggressive lossy codec, compressed using a lossless codec, and/ortransmitted without compression. A storage strategy may define a mannerby which sensor data is stored at the edge device 104. For example,sensor data may be stored permanently (or until a human removes thesensor data), may be stored for a period of time (e.g., one year) or maybe discarded. The method 1000 may be executed by an edge device 104. Themethod 1000 may be executed to reduce the network bandwidth consumed bythe sensor kit 100 and/or reduce the storage constraints at the edgedevice 104.

At 1010, the edge device 104 receives sensor data from the sensors 102of the sensor kit 100. The data may be received continuously orintermittently. In embodiments, the sensors 102 may push the sensor datato the edge device 104 and/or the edge device 104 may request the sensordata 102 from the sensors 102 periodically. In embodiments, the edgedevice 104 may process the sensor data upon receipt, including dedupingthe sensor data.

In embodiments, the edge device 104 may be configured to perform one ormore AI-related tasks prior to transmission via the satellite uplink. Insome of these embodiments, the edge device 104 may be configured todetermine whether there are likely no issues relating to any of thecomponents and/or the industrial setting 120 based on the sensor dataand one or more machine-learned models.

At 1012, the edge device 104 may generate one or more feature vectorsbased on the sensor data. The feature vectors may include sensor datafrom a single sensor 102, a subset of sensors 102, or all of the sensors102 of the sensor kit 100. In scenarios where a single sensor or asubset of sensors 102 are included in the feature vector, themachine-learned model may be trained to identify one or more issuesrelating to an industrial component or the industrial setting 120, butmay not be sufficient to fully deem the entire setting as likelysafe/free from issues. Additionally or alternatively, the featurevectors may correspond to a single snapshot in time (e.g., all sensordata in the feature vector corresponds to the same sampling event) orover a period of time (sensor data samples from a most recent samplingevent and sensor data samples from previous sampling events). Inembodiments where the feature vectors define sensor data from a singlesnapshot, the machine-learned models may be trained to identifypotential issues without any temporal context. In embodiments where thefeature vectors define sensor data over a period of time, themachine-learned models may be trained to identify potential issues withthe context of what the sensor(s) 102 was/were reporting previously. Inthese embodiments, the edge device 104 may maintain a cache of sensordata that is sampled over a predetermined time (e.g., previous hour,previous day, previous N days), such that the cache is cleared out in afirst-in-first-out manner. In these embodiments, the edge device 104 mayretrieve the previous sensor data samples from the cache to use togenerate feature vectors that have data samples spanning a period oftime.

At 1014, the edge device 104 may input the one or more feature vectorsinto one or more respective machine-learned models. A respective modelmay output a prediction or classification relating to an industrialcomponent and/or the industrial setting 120, and a confidence scorerelating to the prediction or classification.

At 1016, the edge device 104 may determine a transmission strategyand/or a storage strategy based on the output of the machine-learnedmodels. In some embodiments, the edge device 104 may make determinationsrelating to the manner by which sensor data is transmitted to thebackend system 150. In some embodiments, the edge device 104 may makedeterminations relating to the manner by which sensor data istransmitted to the backend system 150 and/or stored at the edge device.In some of these embodiments, the edge device 104 may compress sensordata when there are no likely issues across the entire industrialsetting 120 and individual components of the industrial setting 120. Forexample, if the machine-learned models predict that there are likely noissues and classify that there are currently no issues with a highdegree of confidence (e.g., the confidence score is greater than 0.98),the edge device 104 may compress the sensor data. Alternatively, in thescenario where the machine-learned models predict that there are likelyno issues and classify that there are currently no issues with a highdegree of confidence, the edge device 104 may forego transmission butmay store the sensor data at the edge device 104 for a predefined periodof time (e.g., a one-year expiry). In scenarios where a machine-learnedmodel predicts a potential issue or classifies a current issue, the edgedevice 104 may transmit the sensor data without compressing the sensordata or using a lossless compression codec. Additionally oralternatively, in scenarios where a machine-learned model predicts apotential issue or classifies a current issue, the edge device 104 maystore the sensor data used to make the prediction or classificationindefinitely, as well as data that was collected prior to and/or afterthe condition was predicted or classified.

FIGS. 11-15—Exemplary Sensor Kit Configurations

FIG. 11 illustrates an example configuration of a sensor kit 1100according to some embodiments of the present disclosure. In theillustrated example, the sensor kit 1100 is configured to communicatewith a communication network 180 via an uplink 1108 to a satellite 1110.In embodiments, the sensor kit 1100 of FIG. 11 is configured for use inindustrial setting 120 located in remote locations, where cellularcoverage is unreliable or non-existent. In embodiments, the sensor kit1100 may be installed in natural resource extraction, natural resourcetransportation systems, power generation facilities, and the like. Forexample, the sensor kit 1100 may be deployed in an oil or natural gasfields, off-shore oil rigs, mines, oil or gas pipelines, solar fields,wind farms, hydroelectric power stations, and the like.

In the example of FIG. 11, the server kit 1100 includes an edge device104 and a set of sensors 102. The sensors 102 may include various typesof sensors 102, which may vary depending on the industrial setting 120.In the illustrated example, the sensors 102 communicate with the edgedevice 104 via a mesh network. In these embodiments, the sensors 102 maycommunicate sensor data to proximate sensors 102, so as to propagate thesensor data to the edge device 104 located at the remote/peripheralareas of the industrial setting 120 to the edge device 104. While a meshnetwork is shown, the sensor kits 1100 of FIG. 11 may includealternative network topologies, such as a hierarchal topology (e.g.,some or all of the sensors 102 communicate with the edge device 104 viarespective collection devices) or a star topology (e.g., sensors 102communicate to the edge device directly).

In the embodiments of FIG. 11, the edge device 104 includes a satelliteterminal with a directional antenna that communicates with a satellite.The satellite terminal may be pre-configured to communicate with ageosynchronous or low Earth orbit satellites. The edge device 104 mayreceive sensor data from the sensor kit network established by thesensor kit 1100. The edge device 104 may then transmit the sensor datato the backend system 150 via the satellite 1110.

In embodiments, the configurations of the server kit 1100 are suited forindustrial setting 120 covering a remote area where external powersources are not abundant. In embodiments, the sensor kit 1100 mayinclude external power sources, such as batteries, rechargeablebatteries, generators, and/or solar panels. In these embodiments, theexternal power sources may be deployed to power the sensors 102, theedge device 104, and any other devices in the sensor kit 1100.

In embodiments, the configurations of the server kit 1100 are suited foroutdoor industrial setting 120. In embodiments, the sensors 102, theedge device 104, and other devices of the sensor kit 100 (e.g.,collection devices) may be configured with weatherproof housings. Inthese embodiments, the sensor kit 1100 may be deployed in an outdoorsetting.

In embodiments, the edge device 104 may be configured to perform one ormore AI-related tasks prior to transmission via the satellite uplink. Insome of these embodiments, the edge device 104 may be configured todetermine whether there are likely no issues relating to any of thecomponents and/or the industrial setting 120 based on the sensor dataand one or more machine-learned models. In embodiments, the edge device104 may receive the sensor data from the various sensors and maygenerate one or more feature vectors based thereon. The feature vectorsmay include sensor data from a single sensor 102, a subset of sensors102, or all of the sensors 102 of the sensor kit 1100. In scenarioswhere a single sensor or a subset of sensors 102 are included in thefeature vector, the machine-learned model may be trained to identify oneor more issues relating to an industrial component or the industrialsetting 120, but may not be sufficient to fully deem the entire settingas likely safe/free from issues. Additionally or alternatively, thefeature vectors may correspond to a single snapshot in time (e.g., allsensor data in the feature vector corresponds to the same samplingevent) or over a period of time (sensor data samples from a most recentsampling event and sensor data samples from previous sampling events).In embodiments where the feature vectors define sensor data from asingle snapshot, the machine-learned models may be trained to identifypotential issues without any temporal context. In embodiments where thefeature vectors define sensor data over a period of time, themachine-learned models may be trained to identify potential issues withthe context of what the sensor(s) 102 was/were reporting previously. Inthese embodiments, the edge device 104 may maintain a cache of sensordata that is sampled over a predetermined time (e.g., previous hour,previous day, previous N days), such that the cache is cleared out in afirst-in-first-out manner. In these embodiments, the edge device 104 mayretrieve the previous sensor data samples from the cache to use togenerate feature vectors that have data samples spanning a period oftime.

In embodiments, the edge device 104 may feed the one or more featurevectors into one or more respective machine-learned models. A respectivemodel may output a prediction or classification relating to anindustrial component and/or the industrial setting 120, and a confidencescore relating to the prediction or classification. In some embodiments,the edge device 104 may make determinations relating to the manner bywhich sensor data is transmitted to the backend system 150 and/or storedat the edge device. For instance, in some embodiments, the edge device104 may compress sensor data based on the prediction or classification.In some of these embodiments, the edge device 104 may compress sensordata when there are no likely issues across the entire industrialsetting 120 and individual components of the industrial setting 120. Forexample, if the machine-learned models predict that there are likely noissues and classify that there are currently no issues with a highdegree of confidence (e.g., the confidence score is greater than 0.98),the edge device 104 may compress the sensor data. Alternatively, in thescenario where the machine-learned models predict that there are likelyno issues and classify that there are currently no issues with a highdegree of confidence, the edge device 104 may forego transmission butmay store the sensor data at the edge device 104 for a predefined periodof time (e.g., one year). In scenarios where a machine-learned modelpredicts a potential issue or classifies a current issue, the edgedevice 104 may transmit the sensor data without compressing the sensordata or using a lossless compression codec. In this way, the amount ofbandwidth that is transmitted via the satellite uplink may be reduced,as the majority of the time the sensor data will be compressed or nottransmitted.

In embodiments, the edge device 104 may apply one or more rules todetermine whether a triggering condition exists. In embodiments, the oneor more rules may be tailored to identify potentially dangerous and/oremergency situations. In these embodiments, the edge device 104 maytrigger one or more notifications or alarms when a triggering conditionexists. Additionally or alternatively, the edge device 104 may transmitthe sensor data without any compression when a triggering conditionexists.

FIG. 12 illustrates an example configuration of a sensor kit 1200according to some embodiments of the present disclosure. In theillustrated example, the sensor kit 1200 is configured to include agateway device 1206 that communicates with a communication network 180via an uplink 1108 to a satellite 1110. In embodiments, the sensor kit1200 of FIG. 12 is configured for use in industrial setting 120 locatedin remote locations, where cellular coverage is unreliable ornon-existent, and where the edge device 104 is located in a locationwhere physical transmission to a satellite is unreliable or impossible.In embodiments, the sensor kit 1100 may be installed in underground orunderwater facilities, or in facilities having very thick walls. Forexample, the sensor kit 1100 may be deployed in underground mines,underwater oil or gas pipelines, underwater hydroelectric powerstations, and the like.

In the example of FIG. 12, the server kit 1200 includes an edge device104, a set of sensors 102, and a gateway device 1206. In embodiments,the gateway device 1206 is a communication device that includes asatellite terminal with a directional antenna that communicates with asatellite. The satellite terminal may be pre-configured to communicatewith a geosynchronous or low Earth orbit satellites. In embodiments, thegateway device 1206 may communicate with the edge device 104 via a wiredcommunication link 1208 (e.g., Ethernet). The edge device 104 mayreceive sensor data from the sensor kit network established by thesensor kit 1200. The edge device 104 may then transmit the sensor datato the gateway device 1206 via the wired communication link 1208. Thegateway device 1206 may then communicate the sensor data to the backendsystem 150 via the satellite uplink 1108.

The sensors 102 may include various types of sensors 102, which may varydepending on the industrial setting 120. In the illustrated example, thesensors 102 communicate with the edge device 104 via a mesh network. Inthese embodiments, the sensors 102 may communicate sensor data toproximate sensors 102, so as to propagate the sensor data to the edgedevice 104 located at the remote/peripheral areas of the industrialsetting 120 to the edge device 104. While a mesh network is shown, thesensor kits 1200 of FIG. 12 may include alternative network topologies,such as a hierarchal topology (e.g., some or all of the sensors 102communicate with the edge device 104 via respective collection devices)or a star topology (e.g., sensors 102 communicate to the edge devicedirectly).

In embodiments, the configurations of the server kit 1200 are suited forindustrial setting 120 covering a remote area where external powersources are not abundant. In embodiments, the sensor kit 1200 mayinclude external power sources, such as batteries, rechargeablebatteries, generators, and/or solar panels. In these embodiments, theexternal power sources may be deployed to power the sensors 102, theedge device 104, and any other devices in the sensor kit 1200.

In embodiments, the configurations of the server kit 1200 are suited forunderground or underwater industrial setting 120. In embodiments, thesensors 102, the edge device 104, and other devices of the sensor kit100 (e.g., collection devices) may be configured with waterproofhousings or otherwise airtight housings (to prevent dust from enteringthe edge device 104 and/or sensor devices 102). Furthermore, as thegateway device 1208 is likely to be situated outdoors, the gatewaydevice 1208 may include a weatherproof housing.

In embodiments, the edge device 104 may be configured to perform one ormore AI-related tasks prior to transmission via the satellite uplink. Insome of these embodiments, the edge device 104 may be configured todetermine whether there are likely no issues relating to any of thecomponents and/or the industrial setting 120 based on the sensor dataand one or more machine-learned models. In embodiments, the edge device104 may receive the sensor data from the various sensors and maygenerate one or more feature vectors based thereon. The feature vectorsmay include sensor data from a single sensor 102, a subset of sensors102, or all of the sensors 102 of the sensor kit 1200. In scenarioswhere a single sensor or a subset of sensors 102 are included in thefeature vector, the machine-learned model may be trained to identify oneor more issues relating to an industrial component or the industrialsetting 120, but may not be sufficient to fully deem the entire settingas likely safe/free from issues. Additionally or alternatively, thefeature vectors may correspond to a single snapshot in time (e.g., allsensor data in the feature vector corresponds to the same samplingevent) or over a period of time (sensor data samples from a most recentsampling event and sensor data samples from previous sampling events).In embodiments where the feature vectors define sensor data from asingle snapshot, the machine-learned models may be trained to identifypotential issues without any temporal context. In embodiments where thefeature vectors define sensor data over a period of time, themachine-learned models may be trained to identify potential issues withthe context of what the sensor(s) 102 was/were reporting previously. Inthese embodiments, the edge device 104 may maintain a cache of sensordata that is sampled over a predetermined time (e.g., previous hour,previous day, previous N days), such that the cache is cleared out in afirst-in-first-out manner. In these embodiments, the edge device 104 mayretrieve the previous sensor data samples from the cache to use togenerate feature vectors that have data samples spanning a period oftime.

In embodiments, the edge device 104 may feed the one or more featurevectors into one or more respective machine-learned models. A respectivemodel may output a prediction or classification relating to anindustrial component and/or the industrial setting 120, and a confidencescore relating to the prediction or classification. In some embodiments,the edge device 104 may make determinations relating to the manner bywhich sensor data is transmitted to the backend system 150 and/or storedat the edge device. For instance, in some embodiments, the edge device104 may compress sensor data based on the prediction or classification.In some of these embodiments, the edge device 104 may compress sensordata when there are no likely issues across the entire industrialsetting 120 and individual components of the industrial setting 120. Forexample, if the machine-learned models predict that there are likely noissues and classify that there are currently no issues with a highdegree of confidence (e.g., a confidence score is greater than 0.98),the edge device 104 may compress the sensor data. Alternatively, in thescenario where the machine-learned models predict that there are likelyno issues and classify that there are currently no issues with a highdegree of confidence, the edge device 104 may forego transmission butmay store the sensor data at the edge device 104 for a predefined periodof time (e.g., one year). In scenarios where a machine-learned modelpredicts a potential issue or classifies a current issue, the edgedevice 104 may transmit the sensor data without compressing the sensordata or using a lossless compression codec. In this way, the amount ofbandwidth that is transmitted via the satellite uplink may be reduced,as the majority of the time the sensor data will be compressed or nottransmitted.

In embodiments, the edge device 104 may apply one or more rules todetermine whether a triggering condition exists. In embodiments, the oneor more rules may be tailored to identify potentially dangerous and/oremergency situations. In these embodiments, the edge device 104 maytrigger one or more notifications or alarms when a triggering conditionexists. Additionally or alternatively, the edge device 104 may transmitthe sensor data (via the gateway device 1206) without any compressionwhen a triggering condition exists.

FIG. 13 illustrates an example configuration of a server kit 1300according to some embodiments of the present disclosure. In the exampleof FIG. 13, the server kit 1300 includes an edge device 104, a set ofsensors, and a set of collection devices. In embodiments, theconfigurations of the server kit 1300 are suited for industrial setting120 covering a large area and where power sources are abundant; butwhere the industrial operator does not wish to connect the sensor kit1400 to the private network of the industrial setting 120. Inembodiments, the edge device 104 includes a cellular communicationdevice (e.g., a 4G LTE chipset or 5G LTE chipset) with a transceiverthat communicates with a cellular tower 1310. The cellular communicationmay be pre-configured to communicate with a cellular data provider. Forexample, in embodiments, the edge device 104 may include a SIM card thatis registered with a cellular provider having a cellular tower 1310 thatis proximate to the industrial setting 120. The edge device 104 mayreceive sensor data from the sensor kit network established by thesensor kit 1400. The edge device 104 may process the sensor data andthen transmit the sensor data to the backend system 150 via the cellulartower 1310.

The sensors 102 may include various types of sensors 102, which may varydepending on the industrial setting 120. In the illustrated example, thesensors 102 communicate with the edge device 104 via a hierarchicalnetwork. In these embodiments, the sensors 102 may communicate sensordata to collection devices 206, which, in turn, may communicate thesensor data to edge device 104 via a wired or wireless communicationlink. The hierarchical network may be deployed where the area beingmonitored is rather larger (e.g., over 40,000 sq. ft.) and powersupplies are abundant, such as in a factory, a power plant, a foodinspection facility, an indoor grow facility, and the like. While ahierarchal network is shown, the sensor kits 1300 of FIG. 13 may includealternative network topologies, such as a mesh topology or a startopology (e.g., sensors 102 communicate to the edge device directly).

In embodiments, the edge device 104 may be configured to perform one ormore AI-related tasks prior to transmission via the satellite uplink. Insome of these embodiments, the edge device 104 may be configured todetermine whether there are likely no issues relating to any of thecomponents and/or the industrial setting 120 based on the sensor dataand one or more machine-learned models. In embodiments, the edge device104 may receive the sensor data from the various sensors and maygenerate one or more feature vectors based thereon. The feature vectorsmay include sensor data from a single sensor 102, a subset of sensors102, or all of the sensors 102 of the sensor kit 1300. In scenarioswhere a single sensor or a subset of sensors 102 are included in thefeature vector, the machine-learned model may be trained to identify oneor more issues relating to an industrial component or the industrialsetting 120, but may not be sufficient to fully deem the entire settingas likely safe/free from issues. Additionally or alternatively, thefeature vectors may correspond to a single snapshot in time (e.g., allsensor data in the feature vector corresponds to the same samplingevent) or over a period of time (sensor data samples from a most recentsampling event and sensor data samples from previous sampling events).In embodiments where the feature vectors define sensor data from asingle snap shot, the machine-learned models may be trained to identifypotential issues without any temporal context. In embodiments where thefeature vectors define sensor data over a period of time, themachine-learned models may be trained to identify potential issues withthe context of what the sensor(s) 102 was/were reporting previously. Inthese embodiments, the edge device 104 may maintain a cache of sensordata that is sampled over a predetermined time (e.g., previous hour,previous day, previous N days), such that the cache is cleared out in afirst-in-first-out manner. In these embodiments, the edge device 104 mayretrieve the previous sensor data samples from the cache to use togenerate feature vectors that have data samples spanning a period oftime.

In embodiments, the edge device 104 may feed the one or more featurevectors into one or more respective machine-learned models. A respectivemodel may output a prediction or classification relating to anindustrial component and/or the industrial setting 120, and a confidencescore relating to the prediction or classification. In some embodiments,the edge device 104 may make determinations relating to the manner bywhich sensor data is transmitted to the backend system 150 and/or storedat the edge device. For instance, in some embodiments, the edge device104 may compress sensor data based on the prediction or classification.In some of these embodiments, the edge device 104 may compress sensordata when there are no likely issues across the entire industrialsetting 120 and individual components of the industrial setting 120. Forexample, if the machine-learned models predict that there are likely noissues and classify that there are currently no issues with a highdegree of confidence (e.g., a confidence score is greater than 0.98),the edge device 104 may compress the sensor data. Alternatively, in thescenario where the machine-learned models predict that there are likelyno issues and classify that there are currently no issues with a highdegree of confidence, the edge device 104 may forego transmission butmay store the sensor data at the edge device 104 for a predefined periodof time (e.g., one year). In scenarios where a machine-learned modelpredicts a potential issue or classifies a current issue, the edgedevice 104 may transmit the sensor data without compressing the sensordata or using a lossless compression codec. In this way, the amount ofbandwidth that is transmitted via the cellular tower may be reduced, asthe majority of the time the sensor data will be compressed or nottransmitted.

In embodiments, the edge device 104 may apply one or more rules todetermine whether a triggering condition exists. In embodiments, the oneor more rules may be tailored to identify potentially dangerous and/oremergency situations. In these embodiments, the edge device 104 maytrigger one or more notifications or alarms when a triggering conditionexists. Additionally or alternatively, the edge device 104 may transmitthe sensor data without any compression when a triggering conditionexists.

FIG. 14 illustrates an example configuration of a server kit 1400according to some embodiments of the present disclosure. In the exampleof FIG. 14, the server kit 1400 includes an edge device 104, a set ofsensors 102, a set of collection devices 206, and a gateway device 1406.In embodiments, the configurations of the server kit 1400 are suited forindustrial setting 120 covering a large area and where power sources areabundant; but where the industrial operator does not wish to connect thesensor kit 1400 to the private network of the industrial setting 120 andthe walls of the industrial setting 120 make wireless communication(e.g., cellular communication) unreliable or impossible. In embodiments,the gateway device 1406 is a cellular network gateway device thatincludes a cellular communication device (e.g., 4G, 5G chipset) with atransceiver that communicates with a cellular tower 1310. The cellularcommunication may be pre-configured to communicate with a cellular dataprovider. For example, in embodiments, the gateway device may include aSIM card that is registered with a cellular provider having a tower 1310that is proximate to the industrial setting 120. In embodiments, thegateway device 1406 may communicate with the edge device 104 via a wiredcommunication link 1408 (e.g., Ethernet). The edge device 104 mayreceive sensor data from the sensor kit network established by thesensor kit 1400. The edge device 104 may then transmit the sensor datato the gateway device 1406 via the wired communication link 1408. Thegateway device 1406 may then communicate the sensor data to the backendsystem 150 via the cellular tower 1310.

The sensors 102 may include various types of sensors 102, which may varydepending on the industrial setting 120. In the illustrated example, thesensors 102 communicate with the edge device 104 via a hierarchicalnetwork. In these embodiments, the sensors 102 may communicate sensordata to collection devices 206, which, in turn, may communicate thesensor data to edge device 104 via a wired or wireless communicationlink. The hierarchical network may be deployed where the area beingmonitored is rather larger (e.g., over 40,000 sq. ft.) and powersupplies are abundant, such as in a factory, a power plant, a foodinspection facility, an indoor grow facility, and the like. While ahierarchal network is shown, the sensor kits 1400 of FIG. 14 may includealternative network topologies, such as a mesh topology or a startopology (e.g., sensors 102 communicate to the edge device directly).

In embodiments, the edge device 104 may be configured to perform one ormore AI-related tasks prior to transmission via the satellite uplink. Insome of these embodiments, the edge device 104 may be configured todetermine whether there are likely no issues relating to any of thecomponents and/or the industrial setting 120 based on the sensor dataand one or more machine-learned models. In embodiments, the edge device104 may receive the sensor data from the various sensors and maygenerate one or more feature vectors based thereon. The feature vectorsmay include sensor data from a single sensor 102, a subset of sensors102, or all of the sensors 102 of the sensor kit 1400. In scenarioswhere a single sensor or a subset of sensors 102 are included in thefeature vector, the machine-learned model may be trained to identify oneor more issues relating to an industrial component or the industrialsetting 120, but may not be sufficient to fully deem the entire settingas likely safe/free from issues. Additionally or alternatively, thefeature vectors may correspond to a single snapshot in time (e.g., allsensor data in the feature vector corresponds to the same samplingevent) or over a period of time (sensor data samples from a most recentsampling event and sensor data samples from previous sampling events).In embodiments where the feature vectors define sensor data from asingle snapshot, the machine-learned models may be trained to identifypotential issues without any temporal context. In embodiments where thefeature vectors define sensor data over a period of time, themachine-learned models may be trained to identify potential issues withthe context of what the sensor(s) 102 was/were reporting previously. Inthese embodiments, the edge device 104 may maintain a cache of sensordata that is sampled over a predetermined time (e.g., previous hour,previous day, previous N days), such that the cache is cleared out in afirst-in-first-out manner. In these embodiments, the edge device 104 mayretrieve the previous sensor data samples from the cache to use togenerate feature vectors that have data samples spanning a period oftime.

In embodiments, the edge device 104 may feed the one or more featurevectors into one or more respective machine-learned models. A respectivemodel may output a prediction or classification relating to anindustrial component and/or the industrial setting 120, and a confidencescore relating to the prediction or classification. In some embodiments,the edge device 104 may make determinations relating to the manner bywhich sensor data is transmitted to the backend system 150 and/or storedat the edge device. For instance, in some embodiments, the edge device104 may compress sensor data based on the prediction or classification.In some of these embodiments, the edge device 104 may compress sensordata when there are no likely issues across the entire industrialsetting 120 and individual components of the industrial setting 120. Forexample, if the machine-learned models predict that there are likely noissues and classify that there are currently no issues with a highdegree of confidence (e.g., the confidence score is greater than 0.98),the edge device 104 may compress the sensor data. Alternatively, in thescenario where the machine-learned models predict that there are likelyno issues and classify that there are currently no issues with a highdegree of confidence, the edge device 104 may forego transmission butmay store the sensor data at the edge device 104 for a predefined periodof time (e.g., one year). In scenarios where a machine-learned modelpredicts a potential issue or classifies a current issue, the edgedevice 104 may transmit the sensor data without compressing the sensordata or using a lossless compression codec. In this way, the amount ofbandwidth that is transmitted via the cellular tower may be reduced, asthe majority of the time the sensor data will be compressed or nottransmitted.

In embodiments, the edge device 104 may apply one or more rules todetermine whether a triggering condition exists. In embodiments, the oneor more rules may be tailored to identify potentially dangerous and/oremergency situations. In these embodiments, the edge device 104 maytrigger one or more notifications or alarms when a triggering conditionexists. Additionally or alternatively, the edge device 104 may transmitthe sensor data without any compression when a triggering conditionexists.

FIG. 15 illustrates an example configuration of a server kit 1500 forinstallation in an agricultural setting 1520 according to someembodiments of the present disclosure. In the example of FIG. 15, theserver kit 1500 is configured for installation in an indoor agriculturalsetting 1520 that may include, but is not limited to, a control system1522, an HVAC system 1524, a lighting system 1526, a power system 1528,and/or an irrigation system 1530. In this example, various features andcomponents of the agricultural setting include components that aremonitored by a set of sensors 102. In embodiments, the sensors 102capture instances of sensor data and provide the respective instances ofsensor data to an edge device 104. In the example embodiments of FIG.15, the sensor kit 1500 includes a set of collection devices 206 thatroute sensor data from the sensors 102 to the edge device 104. Sensorkits 1500 for deployment in agricultural settings may have differentsensor kit network topologies as well. For instance, in facilities nothaving more than two or three rooms being monitored, the sensor kitnetwork may be a mesh or star network, depending on the distancesbetween the edge device 104 and the furthest potential sensor location.For example, if the distance between the edge device 104 and thefurthest potential sensor location is greater than 150 meters, then thesensor kit network may be configured as a mesh network. In theembodiments of FIG. 15, the edge device 104 transmits the sensor data tothe backend system 150 directly. In these embodiments, the edge device104 includes a cellular communication device that communicates with acellular tower 1310 of a preset cellular provider via a preconfiguredcellular connection to a cellular tower 1310. In other embodiments ofthe disclosure, the edge device 104 transmits the sensor data to thebackend system 150 via a gateway device (e.g., gateway device 1406) thatincludes a cellular communication device that communicates with acellular tower 1310 of a preset cellular provider.

In embodiments, a server kit 1500 may include any suitable combinationof light sensors 1502, weight sensors 1504, temperature sensors 1506,CO2 sensors 1508, humidity sensors 1510, fan speed sensors 1512, and/oraudio/visual (AV) sensors 1514 (e.g., cameras). Sensor kits 1500 may bearranged with additional or alternative sensors 102. In embodiments, thesensor data collected by the edge device 104 may include ambient lightmeasurements indicating an amount of ambient light detected in the areaof a light sensor 1502. In embodiments, the sensor data collected by theedge device 104 may include a weight or mass measurements indicating aweight or mass of an object (e.g., a pot or tray containing one or moreplants) that is resting upon a weight sensor 1504. In embodiments, thesensor data collected by the edge device 104 may include temperaturemeasurements indicating an ambient temperature in the vicinity of atemperature sensor 1506. In embodiments, the sensor data collected bythe edge device 104 may include humidity measurements indicating anambient humidity in the vicinity of a humidity sensor 1510 or moisturemeasurements indicating a relative amount of moisture in a medium (e.g.,soil) monitored by a humidity sensor 1510. In embodiments, the sensordata collected by the edge device 104 may include CO2 measurementsindicating ambient levels of CO2 in the vicinity of a CO2 sensor 1508.In embodiments, the sensor data collected by the edge device 104 mayinclude temperature measurements indicating an ambient temperature inthe vicinity of a temperature sensor 1506. In embodiments, the sensordata collected by the edge device 104 may include fan speed measurementsindicating a measured speed of a fan (e.g., a fan of an HVAC system1524) as measured by a fan speed sensor 1512. In embodiments, the sensordata collected by the edge device 104 may include video signals capturedby an AV sensor 1516. The sensor data captured by sensors 102 andcollected by the edge device 104 may include additional or alternativetypes of sensor data without departing from the scope of the disclosure.

In embodiments, the edge device 104 is configured to perform one or moreedge operations on the sensor data. For example, the edge device 104 maypre-process the received sensor data. In embodiments, the edge device104 may predict or classify potential issues with one or more componentsof the HVAC system 1524, lighting system 1526, power system 1528, theirrigation system 1530; the plants growing in the agricultural facility;and/or the facility itself. In embodiments, the edge device 104 mayanalyze the sensor data with respect to a set of rules that definetriggering conditions. In these embodiments, the edge device 104 maytrigger alarms or notifications in response to a triggering conditionbeing met. In embodiments, the edge device 104 may encode, compress,and/or encrypt the sensor data, prior to transmission to the backendsystem 150. In some of these embodiments, the edge device 104 mayselectively compress the sensor data based on predictions orclassifications made by the edge device 104 and/or upon one or moretriggering conditions being met.

In embodiments, the edge device 104 may be configured to perform one ormore AI-related tasks prior to transmission via the satellite uplink. Insome of these embodiments, the edge device 104 may be configured todetermine whether there are likely no issues relating to any of thecomponents and/or the industrial setting 120 based on the sensor dataand one or more machine-learned models. In embodiments, the edge device104 may receive the sensor data from the various sensors and maygenerate one or more feature vectors based thereon. The feature vectorsmay include sensor data from a single sensor 102, a subset of sensors102, or all of the sensors 102 of the sensor kit 1300. In scenarioswhere a single sensor or a subset of sensors 102 are included in thefeature vector, the machine-learned model may be trained to identify oneor more issues relating to an industrial component or the industrialsetting 120, but may not be sufficient to fully deem the entire settingas likely safe/free from issues. Additionally or alternatively, thefeature vectors may correspond to a single snapshot in time (e.g., allsensor data in the feature vector corresponds to the same samplingevent) or over a period of time (sensor data samples from a most recentsampling event and sensor data samples from previous sampling events).In embodiments where the feature vectors define sensor data from asingle snapshot, the machine-learned models may be trained to identifypotential issues without any temporal context. In embodiments where thefeature vectors define sensor data over a period of time, themachine-learned models may be trained to identify potential issues withthe context of what the sensor(s) 102 was/were reporting previously. Inthese embodiments, the edge device 104 may maintain a cache of sensordata that is sampled over a predetermined time (e.g., previous hour,previous day, previous N days), such that the cache is cleared out in afirst-in-first-out manner. In these embodiments, the edge device 104 mayretrieve the previous sensor data samples from the cache to use togenerate feature vectors that have data samples spanning a period oftime.

In embodiments, the edge device 104 may feed the one or more featurevectors into one or more respective machine-learned models. A respectivemodel may output a prediction or classification relating to anindustrial component and/or the industrial setting 120, and a confidencescore relating to the prediction or classification. In some embodiments,the edge device 104 may make determinations relating to the manner bywhich sensor data is transmitted to the backend system 150 and/or storedat the edge device. For instance, in some embodiments, the edge device104 may compress sensor data based on the prediction or classification.In some of these embodiments, the edge device 104 may compress sensordata when there are no likely issues across the entire industrialsetting 120 and individual components of the industrial setting 120. Forexample, if the machine-learned models predict that there are likely noissues and classify that there are currently no issues with a highdegree of confidence (e.g., the confidence score is greater than 0.98),the edge device 104 may compress the sensor data. Alternatively, in thescenario where the machine-learned models predict that there are likelyno issues and classify that there are currently no issues with a highdegree of confidence, the edge device 104 may forego transmission butmay store the sensor data at the edge device 104 for a predefined periodof time (e.g., one year). In scenarios where a machine-learned modelpredicts a potential issue or classifies a current issue, the edgedevice 104 may transmit the sensor data without compressing the sensordata or using a lossless compression codec. In this way, the amount ofbandwidth that is transmitted via the cellular tower may be reduced, asthe majority of the time the sensor data will be compressed or nottransmitted.

In embodiments, the edge device 104 may apply one or more rules to thesensor data to determine whether a triggering condition exists. Inembodiments, the one or more rules may be tailored to identifypotentially dangerous and/or emergency situations. In these embodiments,the edge device 104 may trigger one or more notifications or alarms whena triggering condition exists. Additionally or alternatively, the edgedevice 104 may transmit the sensor data without any compression when atriggering condition exists. In some embodiments, the edge device 104may selectively compress and/or transmit the sensor data based on theapplication of the one or more rules to the sensor data.

In embodiments, the backend system 150 may perform one or more backendoperations based on received sensor data. In embodiments, the backendsystem 150 may decode/decompress/decrypt the sensor data received fromrespective sensor kits 1500. In embodiments, the backend system 150 maypreprocess received sensor data. In embodiments, the backend system 150may preprocess sensor data received from a respective server kit 1500.For example, the backend system 150 may filter, dedupe, and/or structurethe sensor data. In embodiments, the backend system 150 may perform oneor more AI-related tasks using the sensor data. In some of theseembodiments, the backend system 150 may extract features from the sensordata, which may be used to predict on classify certain conditions orevents relating to the agricultural setting. For example, the backendsystem 150 may deploy models used to predict yields of a crop based onweight measurements, temperature measurements, CO2 measurements, lightmeasurements, and/or other extracted features. In another example, thebackend system 150 may deploy models used to predict or classifymold-inducing states in a room or area of the agricultural facilitybased on temperature measurements, humidity measurements, video signalsor images, and/or other extracted features. In embodiments, the backendsystem 150 may perform one or more analytics tasks on the sensor dataand may display the results to a human user via a dashboard. In someembodiments, the backend system 150 may receive control commands from ahuman user via the dashboard. For example, a human resource withsufficient login credentials may control an HVAC system 1524, a lightingsystem 1526, a power system 1528, and/or an irrigation system 1530 ofthe industrial setting 120. In some of these embodiments, the backendsystem 150 may telemetrically monitor the actions of the human user, andmay train one or more machine-learned models (e.g., neural networks) onactions to take in response to displaying the analytics results to thehuman user. In other embodiments, the backend system 150 may execute oneor more workflows associated with the HVAC system 1524, the lightingsystem 1526, the power system 1528, and/or the irrigation system 1530,in order to control one or more of the systems of the agriculturalsetting 1520 based on a prediction or classification made by the backendsystem in response to the sensor data. In embodiments, the backendsystem 150 provides one or more control commands to a control system1522 of an agricultural setting 1520, which in turn may control the HVACsystem 1524, the lighting system 1526, the power system 1528, and/or theirrigation system 1530 based on the received control commands. Inembodiments, the backend system 150 may provide or utilize an API toprovide control commands to the agricultural setting 1520.

FIG. 16—Exemplary Method of Monitoring Industrial Settings

FIG. 16 illustrates an example set of operations of a method 1600 formonitoring industrial setting 120 using an automatically configuredbackend system 150. In embodiments, the method 1600 may be performed bythe backend system 150, the sensor kit 100, and the dashboard module532.

At 1602, the backend system 150 registers the sensor kit 100 to arespective industrial setting 120. In some embodiments, the backendsystem 150 registers a plurality of sensor kits 100 and registers eachsensor kit 100 of the plurality of sensor kits 100 to a respectiveindustrial setting 120. In embodiments, the backend system 150 providesan interface for specifying a type of entity or industrial setting 120to be monitored. In some embodiments, a user may select a set ofparameters for monitoring of the respective industrial setting 120 ofthe sensor kit 100. The backend system 150 may automatically provision aset of services and capabilities of the backend system 150 based on theselected parameters.

At 1604, the backend system 150 configures the sensor kit 100 to monitorphysical characteristics of the respective industrial setting 120 towhich the sensor kit 100 is registered. For example, when the respectiveindustrial setting 120 is a natural resource extraction setting, thebackend system 150 may configure one or more of infrared sensors, groundpenetrating sensors, light sensors, humidity sensors, temperaturesensors, chemical sensors, fan speed sensors, rotational speed sensors,weight sensors, and camera sensors to monitor and collect sensor datarelating to metrics and parameters of the natural resource extractionsetting and equipment used therein.

At 1606, the sensor kit 100 transmits instances of sensor data to thebackend system 150. In some embodiments, the sensor kit 100 transmitsthe instances of sensor data to the backend system 150 via a gatewaydevice. The gateway device may provide a virtual container for instancesof the sensor data such that only a registered owner or operator of therespective industrial setting 120 can access the sensor data via thebackend system 150.

At 1608, the backend system 150 processes instances of sensor datareceived from the sensor kit 100. In some embodiments, the backendsystem 150 includes an analytics facility and/or a machine learningfacility. The analytics facility and/or the machine learning facilitymay be configured based on the type of the industrial setting 120 andmay process the instances of sensor data received from the sensor kit100. In some embodiments, the backend system 150 updates and/orconfigures a distributed ledger based on the processed instances ofsensor data.

At 1610, the backend system 150 configures and populates the dashboard.In embodiments, the backend system 150 configures the dashboard toretrieve and display one or more of raw sensor data provided by thesensor kit, analytical data relating to the sensor data provided by thesensor kit 100, predictions or classifications made by the backendsystem 150 based on the sensor data, and the like. In some embodiments,the backend system 150 configures alarm limits with respect to one ormore sensor types and/or conditions based on the industrial setting 120.The backend system 150 may define which users receive a notificationwhen an alarm is triggered. In embodiments, the backend system 150 maysubscribe to additional features of the backend system 150 and/or anedge device 104 based on the industrial setting 120.

At 1612, the dashboard provides monitoring information to a human user.In embodiments, the dashboard provides monitoring information to theuser by displaying the monitoring information on a device, e.g., acomputer terminal, a smartphone, a monitor, or any other suitable devicefor displaying information. The monitoring information may be providedvia a graphical user interface.

FIG. 17 illustrates an exemplary manufacturing facility 1700 accordingto some embodiments of the present disclosure. The manufacturingfacility 1700 may include a plurality of industrial machines 1702including, by way of example, conveyor belts, assembly machines, diemachines, turbines, and power systems. The manufacturing facility 1700may further include a plurality of products 1704. The manufacturingfacility may have the sensor kit 100 installed therein, the sensor kit100 including the plurality of sensors 102 and the edge device 104. Byway of example, one or more of the sensors 102 may be installed on someor all of the industrial machines 1702 and the products 1704.

FIG. 18 illustrates a surface portion of an exemplary underwaterindustrial facility 1800 according to some embodiments of the presentdisclosure. The underwater industrial facility 1800 may include atransportation and communication platform 1802, a storage platform 1804,and a pumping platform 1806. The underwater industrial facility 1800 mayhave the sensor kit 100 installed therein, the sensor kit 100 includingthe plurality of sensors 102 and the edge device 104. By way of example,one or more of the sensors 102 may be installed on some or all of thetransportation and communication platform 1802, the storage platform1804, and the pumping platform 1806, and on individual components andmachines thereof.

FIG. 19 illustrates an exemplary indoor agricultural facility 1900according to some embodiments of the present disclosure. The indooragricultural facility 1900 may include a greenhouse 1902 and a pluralityof wind turbines 1904. The indoor agricultural facility 1900 may havethe sensor kit 100 installed therein, the sensor kit 100 including theplurality of sensors 102 and the edge device 104. By way of example, oneor more of the sensors 102 may be installed on some or all components ofthe greenhouse 1904 and on some or all components of the wind turbines1904.

In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network. In embodiments, provided herein are methods and systemsfor monitoring industrial settings, including through a variety of kitsthat provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity including a gateway device that is configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device and having the edge device furtherincludes one or more storage devices that store a sensor data store thatstores instances of sensor data captured by the plurality of sensors ofthe sensor kit. In embodiments, provided herein are methods and systemsfor monitoring industrial settings, including through a variety of kitsthat provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity including a gateway device that is configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device and having the self-configuringsensor kit network is a star network such that each sensor of theplurality of sensors transmits respective instances of sensor data withthe edge device directly using a short-range communication protocol. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having sensors in a self-configuring network and an edgedevice that performs one or more backend operations on sensor dataobtained from the sensor. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and havingsensors and an edge device that stores multiple models and performsAI-related tasks based on sensor data obtained from the sensor using anappropriate model. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity including a gateway device that is configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device and having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a sensor kit and a backend system configured toreceive sensor data collected by the sensor kit and perform one or morebackend operations on the sensor data. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and havingsensors and an edge device that are configured to monitor an indooragricultural setting. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity including a gateway device that is configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device and having sensors and an edgedevice that are configured to monitor a natural resource extractionsetting. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having sensors and an edge device that are configured tomonitor a pipeline setting. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and havingsensors and an edge device that are configured to monitor amanufacturing facility. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity including a gateway device that is configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device and having sensors and an edgedevice that are configured to monitor an underwater industrial setting.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a sensor kit that collects sensor data and abackend system that receives the sensor data from the sensor kits andupdates a distributed ledger based on the sensor data. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having sensors and an edge device that is configured toadd new sensors to the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and havingsensors, an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having an edge device that includes a data processingmodule that deduplicates, filters, flags, and/or aggregates sensor data.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having an edge device that includes an encoding modulethat encodes, compresses, and/or encrypts sensor data according to oneor more media codecs. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity including a gateway device that is configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device and having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having an edge device that includes a notificationmodule that provides notifications and/or alarms to users based onsensor data and/or rules applied to the sensor data. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and having anedge device that includes a distributed ledger module configured toupdate a distributed ledger with sensor data captured by the sensor kit.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a backend system that includes a decoding modulethat decrypts, decodes, and/or decompresses encoded sensor kit packets.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a backend system that includes a data processingmodule that executes a workflow associated with a potential issue basedon sensor data captured by the sensor kit. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security including a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge deviceand having a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and having abackend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and having abackend system that includes an analytics module that performs analyticstasks on sensor data received from the sensor kit. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a backend system that includes a control modulethat provides commands to a device or system in an industrial setting totake remedial action in response to a particular issue being detected.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a backend system that includes a dashboard modulethat presents a dashboard to a human user that provides the human userwith raw sensor data, analytical data, and/or predictions orclassifications based on sensor data received from the sensor kit. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a backend system that includes a dashboard modulethat presents a dashboard to a human user that provides a graphical userinterface that allows the user to configure the sensor kit system. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security including agateway device that is configured to receive sensor kit packets from theedge device via a wired communication link and transmit the sensor kitpackets to the backend system via the public network on behalf of theedge device and having a sensor kit and a backend system that updates asmart contract defining a condition that may trigger an action based onsensor data received from the sensor kit. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security including a gateway devicethat is configured to receive sensor kit packets from the edge devicevia a wired communication link and transmit the sensor kit packets tothe backend system via the public network on behalf of the edge deviceand having a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security including a gateway device that is configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device and havingsensor kit and a backend system that updates a smart contract, whereinthe smart contract verifies one or more conditions put forth by aregulatory body with respect to compliance with a regulation orregulatory action. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity including a gateway device that is configured to receive sensorkit packets from the edge device via a wired communication link andtransmit the sensor kit packets to the backend system via the publicnetwork on behalf of the edge device and having a sensor, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit.

In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and havingthe edge device further includes one or more storage devices that storea sensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having the self-configuring sensor kit network is astar network such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having sensors in a self-configuring network and anedge device that performs one or more backend operations on sensor dataobtained from the sensor. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having sensors and an edge device that storesmultiple models and performs AI-related tasks based on sensor dataobtained from the sensor using an appropriate model. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and havingsensors and an edge device that compresses sensor data collected by thesensor using a media codec. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having a sensor kit and a backend system configuredto receive sensor data collected by the sensor kit and perform one ormore backend operations on the sensor data. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security having the secondcommunication device of the edge device is a satellite terminal devicethat is configured to transmit the sensor kit packets to a satellitethat routes the sensor kits to the public network and having sensors andan edge device that are configured to monitor an indoor agriculturalsetting. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and havingsensors and an edge device that are configured to monitor a naturalresource extraction setting. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having sensors and an edge device that are configuredto monitor a pipeline setting. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having sensors and an edge device that are configuredto monitor a manufacturing facility. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having sensors and an edge device that are configuredto monitor an underwater industrial setting. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security having the secondcommunication device of the edge device is a satellite terminal devicethat is configured to transmit the sensor kit packets to a satellitethat routes the sensor kits to the public network and having a sensorkit that collects sensor data and a backend system that receives thesensor data from the sensor kits and updates a distributed ledger basedon the sensor data. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the second communication device of the edge device is asatellite terminal device that is configured to transmit the sensor kitpackets to a satellite that routes the sensor kits to the public networkand having sensors and an edge device that is configured to add newsensors to the sensor kit. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having sensors, an edge device, and a gateway devicethat communicates with a communication network on behalf of the sensorkit. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and havingan edge device that includes a data processing module that deduplicates,filters, flags, and/or aggregates sensor data. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security having the secondcommunication device of the edge device is a satellite terminal devicethat is configured to transmit the sensor kit packets to a satellitethat routes the sensor kits to the public network and having an edgedevice that includes an encoding module that encodes, compresses, and/orencrypts sensor data according to one or more media codecs. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and havingan edge device that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and havingan edge device that includes a notification module that providesnotifications and/or alarms to users based on sensor data and/or rulesapplied to the sensor data. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having an edge device that includes a distributedledger module configured to update a distributed ledger with sensor datacaptured by the sensor kit. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having a backend system that includes a decodingmodule that decrypts, decodes, and/or decompresses encoded sensor kitpackets. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and having abackend system that includes a data processing module that executes aworkflow associated with a potential issue based on sensor data capturedby the sensor kit. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the second communication device of the edge device is asatellite terminal device that is configured to transmit the sensor kitpackets to a satellite that routes the sensor kits to the public networkand having a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having a backend system that includes a notificationmodule that issues notifications to users when an issue is detected inan industrial setting based on collected sensor data. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and having abackend system that includes an analytics module that performs analyticstasks on sensor data received from the sensor kit. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and having abackend system that includes a control module that provides commands toa device or system in an industrial setting to take remedial action inresponse to a particular issue being detected. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security having the secondcommunication device of the edge device is a satellite terminal devicethat is configured to transmit the sensor kit packets to a satellitethat routes the sensor kits to the public network and having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides the human user with raw sensor data, analyticaldata, and/or predictions or classifications based on sensor datareceived from the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides agraphical user interface that allows the user to configure the sensorkit system. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and having asensor kit and a backend system that includes a configuration modulethat maintains configurations of the sensor kit and configures a sensorkit network by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having thesecond communication device of the edge device is a satellite terminaldevice that is configured to transmit the sensor kit packets to asatellite that routes the sensor kits to the public network and having asensor kit and a backend system that updates a distributed ledger basedon sensor data provided by the sensor kit. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security having the secondcommunication device of the edge device is a satellite terminal devicethat is configured to transmit the sensor kit packets to a satellitethat routes the sensor kits to the public network and having a sensorkit and a backend system that updates a smart contract defining acondition that may trigger an action based on sensor data received fromthe sensor kit. In embodiments, provided herein are methods and systemsfor monitoring industrial settings, including through a variety of kitsthat provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the second communication device of the edge device is asatellite terminal device that is configured to transmit the sensor kitpackets to a satellite that routes the sensor kits to the public networkand having a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having sensor kit and a backend system that updates asmart contract, wherein the smart contract verifies one or moreconditions put forth by a regulatory body with respect to compliancewith a regulation or regulatory action. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the second communication device of the edgedevice is a satellite terminal device that is configured to transmit thesensor kit packets to a satellite that routes the sensor kits to thepublic network and having a sensor, an edge device, and a gateway devicethat communicates with a communication network on behalf of the sensorkit.

In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the edge device further includes one or morestorage devices that store a sensor data store that stores instances ofsensor data captured by the plurality of sensors of the sensor kit andhaving the self-configuring sensor kit network is a star network suchthat each sensor of the plurality of sensors transmits respectiveinstances of sensor data with the edge device directly using ashort-range communication protocol. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the edge device further includes one or morestorage devices that store a sensor data store that stores instances ofsensor data captured by the plurality of sensors of the sensor kit andhaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that stores multiple models and performs AI-related tasks basedon sensor data obtained from the sensor using an appropriate model. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a sensor kit and abackend system configured to receive sensor data collected by the sensorkit and perform one or more backend operations on the sensor data. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that are configured to monitor an indoor agricultural setting. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that are configured to monitor a natural resource extractionsetting. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that are configured to monitor a pipeline setting. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that are configured to monitor a manufacturing facility. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that are configured to monitor an underwater industrial setting.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a sensor kit thatcollects sensor data and a backend system that receives the sensor datafrom the sensor kits and updates a distributed ledger based on thesensor data. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors and an edgedevice that is configured to add new sensors to the sensor kit. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensors, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the edge device further includes one or morestorage devices that store a sensor data store that stores instances ofsensor data captured by the plurality of sensors of the sensor kit andhaving an edge device that includes a data processing module thatdeduplicates, filters, flags, and/or aggregates sensor data. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having an edge device thatincludes an encoding module that encodes, compresses, and/or encryptssensor data according to one or more media codecs. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having an edge device thatincludes a notification module that provides notifications and/or alarmsto users based on sensor data and/or rules applied to the sensor data.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having an edge device thatincludes a configuration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the edge device further includes one or morestorage devices that store a sensor data store that stores instances ofsensor data captured by the plurality of sensors of the sensor kit andhaving an edge device that includes a distributed ledger moduleconfigured to update a distributed ledger with sensor data captured bythe sensor kit. In embodiments, provided herein are methods and systemsfor monitoring industrial settings, including through a variety of kitsthat provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit and having abackend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security having the edge devicefurther includes one or more storage devices that store a sensor datastore that stores instances of sensor data captured by the plurality ofsensors of the sensor kit and having a backend system that includes adata processing module that executes a workflow associated with apotential issue based on sensor data captured by the sensor kit. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a backend system thatincludes an AI module that trains machine-learned models to makepredictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a backend system thatincludes a notification module that issues notifications to users whenan issue is detected in an industrial setting based on collected sensordata. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a backend system thatincludes an analytics module that performs analytics tasks on sensordata received from the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the edge device further includes one or morestorage devices that store a sensor data store that stores instances ofsensor data captured by the plurality of sensors of the sensor kit andhaving a backend system that includes a control module that providescommands to a device or system in an industrial setting to take remedialaction in response to a particular issue being detected. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides the human user with raw sensor data, analytical data,and/or predictions or classifications based on sensor data received fromthe sensor kit. In embodiments, provided herein are methods and systemsfor monitoring industrial settings, including through a variety of kitsthat provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the edge device further includes one or more storagedevices that store a sensor data store that stores instances of sensordata captured by the plurality of sensors of the sensor kit and having abackend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a sensor kit and abackend system that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the edge device further includes one or morestorage devices that store a sensor data store that stores instances ofsensor data captured by the plurality of sensors of the sensor kit andhaving a sensor kit and a backend system that updates a distributedledger based on sensor data provided by the sensor kit. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a sensor kit and abackend system that updates a smart contract defining a condition thatmay trigger an action based on sensor data received from the sensor kit.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a distributed ledgerthat is at least partially shared with a regulatory body to provideinformation related to compliance with a regulation or regulatoryaction. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having sensor kit and abackend system that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theedge device further includes one or more storage devices that store asensor data store that stores instances of sensor data captured by theplurality of sensors of the sensor kit and having a sensor, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit.

In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensors and an edge device that storesmultiple models and performs AI-related tasks based on sensor dataobtained from the sensor using an appropriate model. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensors and an edge device thatcompresses sensor data collected by the sensor using a media codec. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a sensor kit and a backend systemconfigured to receive sensor data collected by the sensor kit andperform one or more backend operations on the sensor data. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensors and an edge device that areconfigured to monitor an indoor agricultural setting. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensors and an edge device that areconfigured to monitor a natural resource extraction setting. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensors and an edge device that areconfigured to monitor a pipeline setting. In embodiments, providedherein are methods and systems for monitoring industrial settings,including through a variety of kits that provide out-of-the-box,self-configuring and automatically provisioned capabilities formonitoring industrial settings while mitigating issues of complexity,integration, bandwidth, latency and security having the self-configuringsensor kit network is a star network such that each sensor of theplurality of sensors transmits respective instances of sensor data withthe edge device directly using a short-range communication protocol andhaving sensors and an edge device that are configured to monitor amanufacturing facility. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having sensors and an edgedevice that are configured to monitor an underwater industrial setting.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a sensor kit that collects sensor dataand a backend system that receives the sensor data from the sensor kitsand updates a distributed ledger based on the sensor data. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensors and an edge device that isconfigured to add new sensors to the sensor kit. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensors, an edge device, and a gatewaydevice that communicates with a communication network on behalf of thesensor kit. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having an edge device that includes a dataprocessing module that deduplicates, filters, flags, and/or aggregatessensor data. In embodiments, provided herein are methods and systems formonitoring industrial settings, including through a variety of kits thatprovide out-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having an edge device that includes anencoding module that encodes, compresses, and/or encrypts sensor dataaccording to one or more media codecs. In embodiments, provided hereinare methods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the self-configuring sensor kit network is astar network such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the self-configuring sensor kit network is astar network such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a backend system that includes adecoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having a backend system thatincludes a data processing module that executes a workflow associatedwith a potential issue based on sensor data captured by the sensor kit.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a backend system that includes an AImodule that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a backend system that includes anotification module that issues notifications to users when an issue isdetected in an industrial setting based on collected sensor data. Inembodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit. In embodiments, provided herein are methods andsystems for monitoring industrial settings, including through a varietyof kits that provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having a backend system thatincludes a control module that provides commands to a device or systemin an industrial setting to take remedial action in response to aparticular issue being detected. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the self-configuring sensor kit network is astar network such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides the human user with raw sensor data, analytical data,and/or predictions or classifications based on sensor data received fromthe sensor kit. In embodiments, provided herein are methods and systemsfor monitoring industrial settings, including through a variety of kitsthat provide out-of-the-box, self-configuring and automaticallyprovisioned capabilities for monitoring industrial settings whilemitigating issues of complexity, integration, bandwidth, latency andsecurity having the self-configuring sensor kit network is a starnetwork such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides a graphical user interface that allows the user toconfigure the sensor kit system. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the self-configuring sensor kit network is astar network such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having a sensor kit and abackend system that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein aremethods and systems for monitoring industrial settings, includingthrough a variety of kits that provide out-of-the-box, self-configuringand automatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the self-configuring sensor kit network is astar network such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having a sensor kit and abackend system that updates a distributed ledger based on sensor dataprovided by the sensor kit. In embodiments, provided herein are methodsand systems for monitoring industrial settings, including through avariety of kits that provide out-of-the-box, self-configuring andautomatically provisioned capabilities for monitoring industrialsettings while mitigating issues of complexity, integration, bandwidth,latency and security having the self-configuring sensor kit network is astar network such that each sensor of the plurality of sensors transmitsrespective instances of sensor data with the edge device directly usinga short-range communication protocol and having a sensor kit and abackend system that updates a smart contract defining a condition thatmay trigger an action based on sensor data received from the sensor kit.In embodiments, provided herein are methods and systems for monitoringindustrial settings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a distributed ledger that is at leastpartially shared with a regulatory body to provide information relatedto compliance with a regulation or regulatory action. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having sensor kit and a backend system thatupdates a smart contract, wherein the smart contract verifies one ormore conditions put forth by a regulatory body with respect tocompliance with a regulation or regulatory action. In embodiments,provided herein are methods and systems for monitoring industrialsettings, including through a variety of kits that provideout-of-the-box, self-configuring and automatically provisionedcapabilities for monitoring industrial settings while mitigating issuesof complexity, integration, bandwidth, latency and security having theself-configuring sensor kit network is a star network such that eachsensor of the plurality of sensors transmits respective instances ofsensor data with the edge device directly using a short-rangecommunication protocol and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and havingsensors and an edge device that stores multiple models and performsAI-related tasks based on sensor data obtained from the sensor using anappropriate model. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having sensors and an edge device that compresses sensor datacollected by the sensor using a media codec. In embodiments, providedherein is a sensor kit having sensors in a self-configuring network andan edge device that performs one or more backend operations on sensordata obtained from the sensor and having a sensor kit and a backendsystem configured to receive sensor data collected by the sensor kit andperform one or more backend operations on the sensor data. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and havingsensors and an edge device that are configured to monitor an indooragricultural setting. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having sensors and an edge device that are configured tomonitor a natural resource extraction setting. In embodiments, providedherein is a sensor kit having sensors in a self-configuring network andan edge device that performs one or more backend operations on sensordata obtained from the sensor and having sensors and an edge device thatare configured to monitor a pipeline setting. In embodiments, providedherein is a sensor kit having sensors in a self-configuring network andan edge device that performs one or more backend operations on sensordata obtained from the sensor and having sensors and an edge device thatare configured to monitor a manufacturing facility. In embodiments,provided herein is a sensor kit having sensors in a self-configuringnetwork and an edge device that performs one or more backend operationson sensor data obtained from the sensor and having sensors and an edgedevice that are configured to monitor an underwater industrial setting.In embodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and having asensor kit that collects sensor data and a backend system that receivesthe sensor data from the sensor kits and updates a distributed ledgerbased on the sensor data. In embodiments, provided herein is a sensorkit having sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having sensors and an edge device that is configured to addnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors in a self-configuring network and an edgedevice that performs one or more backend operations on sensor dataobtained from the sensor and having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having an edge device that includes a data processing modulethat deduplicates, filters, flags, and/or aggregates sensor data. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and having anedge device that includes an encoding module that encodes, compresses,and/or encrypts sensor data according to one or more media codecs. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and having anedge device that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data. In embodiments, provided herein is a sensor kit havingsensors in a self-configuring network and an edge device that performsone or more backend operations on sensor data obtained from the sensorand having an edge device that includes a notification module thatprovides notifications and/or alarms to users based on sensor dataand/or rules applied to the sensor data. In embodiments, provided hereinis a sensor kit having sensors in a self-configuring network and an edgedevice that performs one or more backend operations on sensor dataobtained from the sensor and having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors in a self-configuring network and an edgedevice that performs one or more backend operations on sensor dataobtained from the sensor and having an edge device that includes adistributed ledger module configured to update a distributed ledger withsensor data captured by the sensor kit. In embodiments, provided hereinis a sensor kit having sensors in a self-configuring network and an edgedevice that performs one or more backend operations on sensor dataobtained from the sensor and having a backend system that includes adecoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having a backend system that includes a data processingmodule that executes a workflow associated with a potential issue basedon sensor data captured by the sensor kit. In embodiments, providedherein is a sensor kit having sensors in a self-configuring network andan edge device that performs one or more backend operations on sensordata obtained from the sensor and having a backend system that includesan AI module that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and having abackend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having sensors in a self-configuring network and an edgedevice that performs one or more backend operations on sensor dataobtained from the sensor and having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having a backend system that includes a control module thatprovides commands to a device or system in an industrial setting to takeremedial action in response to a particular issue being detected. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and having abackend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors in a self-configuring network andan edge device that performs one or more backend operations on sensordata obtained from the sensor and having a backend system that includesa dashboard module that presents a dashboard to a human user thatprovides a graphical user interface that allows the user to configurethe sensor kit system. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and having asensor kit and a backend system that updates a distributed ledger basedon sensor data provided by the sensor kit. In embodiments, providedherein is a sensor kit having sensors in a self-configuring network andan edge device that performs one or more backend operations on sensordata obtained from the sensor and having a sensor kit and a backendsystem that updates a smart contract defining a condition that maytrigger an action based on sensor data received from the sensor kit. Inembodiments, provided herein is a sensor kit having sensors in aself-configuring network and an edge device that performs one or morebackend operations on sensor data obtained from the sensor and having adistributed ledger that is at least partially shared with a regulatorybody to provide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having sensor kit and a backend system that updates a smartcontract, wherein the smart contract verifies one or more conditions putforth by a regulatory body with respect to compliance with a regulationor regulatory action. In embodiments, provided herein is a sensor kithaving sensors in a self-configuring network and an edge device thatperforms one or more backend operations on sensor data obtained from thesensor and having a sensor, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that stores multiple models and performs AI-related tasksbased on sensor data obtained from the sensor using an appropriatemodel. In embodiments, provided herein is a sensor kit having sensorsand an edge device that stores multiple models and performs AI-relatedtasks based on sensor data obtained from the sensor using an appropriatemodel and having sensors and an edge device that compresses sensor datacollected by the sensor using a media codec. In embodiments, providedherein is a sensor kit having sensors and an edge device that storesmultiple models and performs AI-related tasks based on sensor dataobtained from the sensor using an appropriate model and having a sensorkit and a backend system configured to receive sensor data collected bythe sensor kit and perform one or more backend operations on the sensordata. In embodiments, provided herein is a sensor kit having sensors andan edge device that stores multiple models and performs AI-related tasksbased on sensor data obtained from the sensor using an appropriate modeland having sensors and an edge device that are configured to monitor anindoor agricultural setting. In embodiments, provided herein is a sensorkit having sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having sensors and an edge device thatare configured to monitor a natural resource extraction setting. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that stores multiple models and performs AI-related tasks basedon sensor data obtained from the sensor using an appropriate model andhaving sensors and an edge device that are configured to monitor apipeline setting. In embodiments, provided herein is a sensor kit havingsensors and an edge device that stores multiple models and performsAI-related tasks based on sensor data obtained from the sensor using anappropriate model and having sensors and an edge device that areconfigured to monitor a manufacturing facility. In embodiments, providedherein is a sensor kit having sensors and an edge device that storesmultiple models and performs AI-related tasks based on sensor dataobtained from the sensor using an appropriate model and having sensorsand an edge device that are configured to monitor an underwaterindustrial setting. In embodiments, provided herein is a sensor kithaving sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having a sensor kit that collects sensordata and a backend system that receives the sensor data from the sensorkits and updates a distributed ledger based on the sensor data. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that stores multiple models and performs AI-related tasks basedon sensor data obtained from the sensor using an appropriate model andhaving sensors and an edge device that is configured to add new sensorsto the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having an edge device that includes adata processing module that deduplicates, filters, flags, and/oraggregates sensor data. In embodiments, provided herein is a sensor kithaving sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having an edge device that includes anencoding module that encodes, compresses, and/or encrypts sensor dataaccording to one or more media codecs. In embodiments, provided hereinis a sensor kit having sensors and an edge device that stores multiplemodels and performs AI-related tasks based on sensor data obtained fromthe sensor using an appropriate model and having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data. In embodiments,provided herein is a sensor kit having sensors and an edge device thatstores multiple models and performs AI-related tasks based on sensordata obtained from the sensor using an appropriate model and having anedge device that includes a notification module that providesnotifications and/or alarms to users based on sensor data and/or rulesapplied to the sensor data. In embodiments, provided herein is a sensorkit having sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that stores multiple modelsand performs AI-related tasks based on sensor data obtained from thesensor using an appropriate model and having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatstores multiple models and performs AI-related tasks based on sensordata obtained from the sensor using an appropriate model and having abackend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets. In embodiments, providedherein is a sensor kit having sensors and an edge device that storesmultiple models and performs AI-related tasks based on sensor dataobtained from the sensor using an appropriate model and having a backendsystem that includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that stores multiple models and performsAI-related tasks based on sensor data obtained from the sensor using anappropriate model and having a backend system that includes an AI modulethat trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that stores multiple models and performs AI-related tasks basedon sensor data obtained from the sensor using an appropriate model andhaving a backend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having sensors and an edge device that stores multiplemodels and performs AI-related tasks based on sensor data obtained fromthe sensor using an appropriate model and having a backend system thatincludes an analytics module that performs analytics tasks on sensordata received from the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that stores multiple modelsand performs AI-related tasks based on sensor data obtained from thesensor using an appropriate model and having a backend system thatincludes a control module that provides commands to a device or systemin an industrial setting to take remedial action in response to aparticular issue being detected. In embodiments, provided herein is asensor kit having sensors and an edge device that stores multiple modelsand performs AI-related tasks based on sensor data obtained from thesensor using an appropriate model and having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides the human user with raw sensor data, analytical data,and/or predictions or classifications based on sensor data received fromthe sensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that stores multiple models and performsAI-related tasks based on sensor data obtained from the sensor using anappropriate model and having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides agraphical user interface that allows the user to configure the sensorkit system. In embodiments, provided herein is a sensor kit havingsensors and an edge device that stores multiple models and performsAI-related tasks based on sensor data obtained from the sensor using anappropriate model and having a sensor kit and a backend system thatincludes a configuration module that maintains configurations of thesensor kit and configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein is a sensorkit having sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having a sensor kit and a backend systemthat updates a distributed ledger based on sensor data provided by thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that stores multiple models and performsAI-related tasks based on sensor data obtained from the sensor using anappropriate model and having a sensor kit and a backend system thatupdates a smart contract defining a condition that may trigger an actionbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatstores multiple models and performs AI-related tasks based on sensordata obtained from the sensor using an appropriate model and having adistributed ledger that is at least partially shared with a regulatorybody to provide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that stores multiple models andperforms AI-related tasks based on sensor data obtained from the sensorusing an appropriate model and having sensor kit and a backend systemthat updates a smart contract, wherein the smart contract verifies oneor more conditions put forth by a regulatory body with respect tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having sensors and an edge device thatstores multiple models and performs AI-related tasks based on sensordata obtained from the sensor using an appropriate model and having asensor, an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that compresses sensor data collected by the sensor using amedia codec. In embodiments, provided herein is a sensor kit havingsensors and an edge device that compresses sensor data collected by thesensor using a media codec and having a sensor kit and a backend systemconfigured to receive sensor data collected by the sensor kit andperform one or more backend operations on the sensor data. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having sensors and an edge device that are configured tomonitor an indoor agricultural setting. In embodiments, provided hereinis a sensor kit having sensors and an edge device that compresses sensordata collected by the sensor using a media codec and having sensors andan edge device that are configured to monitor a natural resourceextraction setting. In embodiments, provided herein is a sensor kithaving sensors and an edge device that compresses sensor data collectedby the sensor using a media codec and having sensors and an edge devicethat are configured to monitor a pipeline setting. In embodiments,provided herein is a sensor kit having sensors and an edge device thatcompresses sensor data collected by the sensor using a media codec andhaving sensors and an edge device that are configured to monitor amanufacturing facility. In embodiments, provided herein is a sensor kithaving sensors and an edge device that compresses sensor data collectedby the sensor using a media codec and having sensors and an edge devicethat are configured to monitor an underwater industrial setting. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a sensor kit that collects sensor data and a backendsystem that receives the sensor data from the sensor kits and updates adistributed ledger based on the sensor data. In embodiments, providedherein is a sensor kit having sensors and an edge device that compressessensor data collected by the sensor using a media codec and havingsensors and an edge device that is configured to add new sensors to thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that compresses sensor data collected by thesensor using a media codec and having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that compresses sensor data collectedby the sensor using a media codec and having an edge device thatincludes a data processing module that deduplicates, filters, flags,and/or aggregates sensor data. In embodiments, provided herein is asensor kit having sensors and an edge device that compresses sensor datacollected by the sensor using a media codec and having an edge devicethat includes an encoding module that encodes, compresses, and/orencrypts sensor data according to one or more media codecs. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having an edge device that includes a quick-decision AI modulethat uses machine-learned models to generate predictions related toand/or classifications of industrial components based on features ofcollected sensor data. In embodiments, provided herein is a sensor kithaving sensors and an edge device that compresses sensor data collectedby the sensor using a media codec and having an edge device thatincludes a notification module that provides notifications and/or alarmsto users based on sensor data and/or rules applied to the sensor data.In embodiments, provided herein is a sensor kit having sensors and anedge device that compresses sensor data collected by the sensor using amedia codec and having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein is a sensorkit having sensors and an edge device that compresses sensor datacollected by the sensor using a media codec and having an edge devicethat includes a distributed ledger module configured to update adistributed ledger with sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a backend system that includes a decoding module thatdecrypts, decodes, and/or decompresses encoded sensor kit packets. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a backend system that includes a data processing modulethat executes a workflow associated with a potential issue based onsensor data captured by the sensor kit. In embodiments, provided hereinis a sensor kit having sensors and an edge device that compresses sensordata collected by the sensor using a media codec and having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that compresses sensor data collected by thesensor using a media codec and having a backend system that includes anotification module that issues notifications to users when an issue isdetected in an industrial setting based on collected sensor data. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a backend system that includes an analytics module thatperforms analytics tasks on sensor data received from the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a backend system that includes a control module thatprovides commands to a device or system in an industrial setting to takeremedial action in response to a particular issue being detected. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a backend system that includes a dashboard module thatpresents a dashboard to a human user that provides the human user withraw sensor data, analytical data, and/or predictions or classificationsbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatcompresses sensor data collected by the sensor using a media codec andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit having sensors and an edge device thatcompresses sensor data collected by the sensor using a media codec andhaving a sensor kit and a backend system that includes a configurationmodule that maintains configurations of the sensor kit and configures asensor kit network by transmitting configuration requests to sensordevices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a sensor kit and a backend system that updates a smartcontract defining a condition that may trigger an action based on sensordata received from the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that compresses sensor datacollected by the sensor using a media codec and having a distributedledger that is at least partially shared with a regulatory body toprovide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that compresses sensor data collectedby the sensor using a media codec and having sensor kit and a backendsystem that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that compresses sensor data collected by the sensor using a mediacodec and having a sensor, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a sensorkit and a backend system configured to receive sensor data collected bythe sensor kit and perform one or more backend operations on the sensordata. In embodiments, provided herein is a sensor kit system having asensor kit and a backend system configured to receive sensor datacollected by the sensor kit and perform one or more backend operationson the sensor data and having sensors and an edge device that areconfigured to monitor an indoor agricultural setting. In embodiments,provided herein is a sensor kit system having a sensor kit and a backendsystem configured to receive sensor data collected by the sensor kit andperform one or more backend operations on the sensor data and havingsensors and an edge device that are configured to monitor a naturalresource extraction setting. In embodiments, provided herein is a sensorkit system having a sensor kit and a backend system configured toreceive sensor data collected by the sensor kit and perform one or morebackend operations on the sensor data and having sensors and an edgedevice that are configured to monitor a pipeline setting. Inembodiments, provided herein is a sensor kit system having a sensor kitand a backend system configured to receive sensor data collected by thesensor kit and perform one or more backend operations on the sensor dataand having sensors and an edge device that are configured to monitor amanufacturing facility. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system configured to receivesensor data collected by the sensor kit and perform one or more backendoperations on the sensor data and having sensors and an edge device thatare configured to monitor an underwater industrial setting. Inembodiments, provided herein is a sensor kit system having a sensor kitand a backend system configured to receive sensor data collected by thesensor kit and perform one or more backend operations on the sensor dataand having a sensor kit that collects sensor data and a backend systemthat receives the sensor data from the sensor kits and updates adistributed ledger based on the sensor data. In embodiments, providedherein is a sensor kit system having a sensor kit and a backend systemconfigured to receive sensor data collected by the sensor kit andperform one or more backend operations on the sensor data and havingsensors and an edge device that is configured to add new sensors to thesensor kit. In embodiments, provided herein is a sensor kit systemhaving a sensor kit and a backend system configured to receive sensordata collected by the sensor kit and perform one or more backendoperations on the sensor data and having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system configured to receivesensor data collected by the sensor kit and perform one or more backendoperations on the sensor data and having an edge device that includes adata processing module that deduplicates, filters, flags, and/oraggregates sensor data. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system configured to receivesensor data collected by the sensor kit and perform one or more backendoperations on the sensor data and having an edge device that includes anencoding module that encodes, compresses, and/or encrypts sensor dataaccording to one or more media codecs. In embodiments, provided hereinis a sensor kit system having a sensor kit and a backend systemconfigured to receive sensor data collected by the sensor kit andperform one or more backend operations on the sensor data and having anedge device that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data. In embodiments, provided herein is a sensor kit systemhaving a sensor kit and a backend system configured to receive sensordata collected by the sensor kit and perform one or more backendoperations on the sensor data and having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data. Inembodiments, provided herein is a sensor kit system having a sensor kitand a backend system configured to receive sensor data collected by thesensor kit and perform one or more backend operations on the sensor dataand having an edge device that includes a configuration module thatconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a sensor kitand a backend system configured to receive sensor data collected by thesensor kit and perform one or more backend operations on the sensor dataand having an edge device that includes a distributed ledger moduleconfigured to update a distributed ledger with sensor data captured bythe sensor kit. In embodiments, provided herein is a sensor kit systemhaving a sensor kit and a backend system configured to receive sensordata collected by the sensor kit and perform one or more backendoperations on the sensor data and having a backend system that includesa decoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system configured to receivesensor data collected by the sensor kit and perform one or more backendoperations on the sensor data and having a backend system that includesa data processing module that executes a workflow associated with apotential issue based on sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit system having a sensor kitand a backend system configured to receive sensor data collected by thesensor kit and perform one or more backend operations on the sensor dataand having a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit. In embodiments, provided herein isa sensor kit system having a sensor kit and a backend system configuredto receive sensor data collected by the sensor kit and perform one ormore backend operations on the sensor data and having a backend systemthat includes a notification module that issues notifications to userswhen an issue is detected in an industrial setting based on collectedsensor data. In embodiments, provided herein is a sensor kit systemhaving a sensor kit and a backend system configured to receive sensordata collected by the sensor kit and perform one or more backendoperations on the sensor data and having a backend system that includesan analytics module that performs analytics tasks on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system configured toreceive sensor data collected by the sensor kit and perform one or morebackend operations on the sensor data and having a backend system thatincludes a control module that provides commands to a device or systemin an industrial setting to take remedial action in response to aparticular issue being detected. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system configured toreceive sensor data collected by the sensor kit and perform one or morebackend operations on the sensor data and having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides the human user with raw sensor data, analytical data,and/or predictions or classifications based on sensor data received fromthe sensor kit. In embodiments, provided herein is a sensor kit systemhaving a sensor kit and a backend system configured to receive sensordata collected by the sensor kit and perform one or more backendoperations on the sensor data and having a backend system that includesa dashboard module that presents a dashboard to a human user thatprovides a graphical user interface that allows the user to configurethe sensor kit system. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system configured to receivesensor data collected by the sensor kit and perform one or more backendoperations on the sensor data and having a sensor kit and a backendsystem that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system configured toreceive sensor data collected by the sensor kit and perform one or morebackend operations on the sensor data and having a sensor kit and abackend system that updates a distributed ledger based on sensor dataprovided by the sensor kit. In embodiments, provided herein is a sensorkit system having a sensor kit and a backend system configured toreceive sensor data collected by the sensor kit and perform one or morebackend operations on the sensor data and having a sensor kit and abackend system that updates a smart contract defining a condition thatmay trigger an action based on sensor data received from the sensor kit.In embodiments, provided herein is a sensor kit system having a sensorkit and a backend system configured to receive sensor data collected bythe sensor kit and perform one or more backend operations on the sensordata and having a distributed ledger that is at least partially sharedwith a regulatory body to provide information related to compliance witha regulation or regulatory action. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system configured toreceive sensor data collected by the sensor kit and perform one or morebackend operations on the sensor data and having sensor kit and abackend system that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit system having a sensor kitand a backend system configured to receive sensor data collected by thesensor kit and perform one or more backend operations on the sensor dataand having a sensor, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor an indoor agriculturalsetting. In embodiments, provided herein is a sensor kit having sensorsand an edge device that are configured to monitor an indoor agriculturalsetting and having sensors and an edge device that are configured tomonitor a natural resource extraction setting. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an indoor agricultural setting and having sensorsand an edge device that are configured to monitor a pipeline setting. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving sensors and an edge device that are configured to monitor amanufacturing facility. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anindoor agricultural setting and having sensors and an edge device thatare configured to monitor an underwater industrial setting. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving a sensor kit that collects sensor data and a backend system thatreceives the sensor data from the sensor kits and updates a distributedledger based on the sensor data. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor an indoor agricultural setting and having sensors and an edgedevice that is configured to add new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving sensors, an edge device, and a gateway device that communicateswith a communication network on behalf of the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving an edge device that includes a data processing module thatdeduplicates, filters, flags, and/or aggregates sensor data. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving an edge device that includes an encoding module that encodes,compresses, and/or encrypts sensor data according to one or more mediacodecs. In embodiments, provided herein is a sensor kit having sensorsand an edge device that are configured to monitor an indoor agriculturalsetting and having an edge device that includes a quick-decision AImodule that uses machine-learned models to generate predictions relatedto and/or classifications of industrial components based on features ofcollected sensor data. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anindoor agricultural setting and having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving an edge device that includes a configuration module thatconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving an edge device that includes a distributed ledger moduleconfigured to update a distributed ledger with sensor data captured bythe sensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor an indooragricultural setting and having a backend system that includes adecoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anindoor agricultural setting and having a backend system that includes adata processing module that executes a workflow associated with apotential issue based on sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit. In embodiments, provided herein isa sensor kit having sensors and an edge device that are configured tomonitor an indoor agricultural setting and having a backend system thatincludes a notification module that issues notifications to users whenan issue is detected in an industrial setting based on collected sensordata. In embodiments, provided herein is a sensor kit having sensors andan edge device that are configured to monitor an indoor agriculturalsetting and having a backend system that includes an analytics modulethat performs analytics tasks on sensor data received from the sensorkit. In embodiments, provided herein is a sensor kit having sensors andan edge device that are configured to monitor an indoor agriculturalsetting and having a backend system that includes a control module thatprovides commands to a device or system in an industrial setting to takeremedial action in response to a particular issue being detected. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an indoor agricultural setting andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an indoor agricultural setting and having abackend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an indoor agricultural setting and having asensor kit and a backend system that includes a configuration modulethat maintains configurations of the sensor kit and configures a sensorkit network by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an indoor agricultural setting and having asensor kit and a backend system that updates a distributed ledger basedon sensor data provided by the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an indoor agricultural setting and having a sensorkit and a backend system that updates a smart contract defining acondition that may trigger an action based on sensor data received fromthe sensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor an indooragricultural setting and having a distributed ledger that is at leastpartially shared with a regulatory body to provide information relatedto compliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an indoor agricultural setting and havingsensor kit and a backend system that updates a smart contract, whereinthe smart contract verifies one or more conditions put forth by aregulatory body with respect to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anindoor agricultural setting and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a natural resource extractionsetting. In embodiments, provided herein is a sensor kit having sensorsand an edge device that are configured to monitor a natural resourceextraction setting and having sensors and an edge device that areconfigured to monitor a pipeline setting. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a natural resource extraction setting and havingsensors and an edge device that are configured to monitor amanufacturing facility. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anatural resource extraction setting and having sensors and an edgedevice that are configured to monitor an underwater industrial setting.In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a natural resource extractionsetting and having a sensor kit that collects sensor data and a backendsystem that receives the sensor data from the sensor kits and updates adistributed ledger based on the sensor data. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a natural resource extraction setting and havingsensors and an edge device that is configured to add new sensors to thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor a naturalresource extraction setting and having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anatural resource extraction setting and having an edge device thatincludes a data processing module that deduplicates, filters, flags,and/or aggregates sensor data. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor a natural resource extraction setting and having an edge devicethat includes an encoding module that encodes, compresses, and/orencrypts sensor data according to one or more media codecs. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a natural resource extractionsetting and having an edge device that includes a quick-decision AImodule that uses machine-learned models to generate predictions relatedto and/or classifications of industrial components based on features ofcollected sensor data. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anatural resource extraction setting and having an edge device thatincludes a notification module that provides notifications and/or alarmsto users based on sensor data and/or rules applied to the sensor data.In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a natural resource extractionsetting and having an edge device that includes a configuration modulethat configures a sensor kit network by transmitting configurationrequests to sensor devices, generating device records based on responsesto the configuration requests, and/or adding new sensors to the sensorkit. In embodiments, provided herein is a sensor kit having sensors andan edge device that are configured to monitor a natural resourceextraction setting and having an edge device that includes a distributedledger module configured to update a distributed ledger with sensor datacaptured by the sensor kit. In embodiments, provided herein is a sensorkit having sensors and an edge device that are configured to monitor anatural resource extraction setting and having a backend system thatincludes a decoding module that decrypts, decodes, and/or decompressesencoded sensor kit packets. In embodiments, provided herein is a sensorkit having sensors and an edge device that are configured to monitor anatural resource extraction setting and having a backend system thatincludes a data processing module that executes a workflow associatedwith a potential issue based on sensor data captured by the sensor kit.In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a natural resource extractionsetting and having a backend system that includes an AI module thattrains machine-learned models to make predictions or classificationsrelated to sensor data captured by a sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a natural resource extraction setting andhaving a backend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having sensors and an edge device that are configured tomonitor a natural resource extraction setting and having a backendsystem that includes an analytics module that performs analytics taskson sensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a natural resource extraction setting and having abackend system that includes a control module that provides commands toa device or system in an industrial setting to take remedial action inresponse to a particular issue being detected. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a natural resource extraction setting and having abackend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a natural resource extraction setting and having abackend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a natural resource extraction setting andhaving a sensor kit and a backend system that includes a configurationmodule that maintains configurations of the sensor kit and configures asensor kit network by transmitting configuration requests to sensordevices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a natural resource extractionsetting and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a natural resource extractionsetting and having a sensor kit and a backend system that updates asmart contract defining a condition that may trigger an action based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a natural resource extraction setting and having adistributed ledger that is at least partially shared with a regulatorybody to provide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anatural resource extraction setting and having sensor kit and a backendsystem that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a natural resource extractionsetting and having a sensor, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a pipeline setting. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a pipeline setting and havingsensors and an edge device that are configured to monitor amanufacturing facility. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor apipeline setting and having sensors and an edge device that areconfigured to monitor an underwater industrial setting. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a pipeline setting and having a sensor kitthat collects sensor data and a backend system that receives the sensordata from the sensor kits and updates a distributed ledger based on thesensor data. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor a pipelinesetting and having sensors and an edge device that is configured to addnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor a pipeline setting and having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor apipeline setting and having an edge device that includes a dataprocessing module that deduplicates, filters, flags, and/or aggregatessensor data. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor a pipelinesetting and having an edge device that includes an encoding module thatencodes, compresses, and/or encrypts sensor data according to one ormore media codecs. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor apipeline setting and having an edge device that includes aquick-decision AI module that uses machine-learned models to generatepredictions related to and/or classifications of industrial componentsbased on features of collected sensor data. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a pipeline setting and having an edge device thatincludes a notification module that provides notifications and/or alarmsto users based on sensor data and/or rules applied to the sensor data.In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a pipeline setting and havingan edge device that includes a configuration module that configures asensor kit network by transmitting configuration requests to sensordevices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a pipeline setting and having anedge device that includes a distributed ledger module configured toupdate a distributed ledger with sensor data captured by the sensor kit.In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a pipeline setting and havinga backend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a pipeline setting and having a backend systemthat includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor a pipelinesetting and having a backend system that includes an AI module thattrains machine-learned models to make predictions or classificationsrelated to sensor data captured by a sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a pipeline setting and having a backend systemthat includes a notification module that issues notifications to userswhen an issue is detected in an industrial setting based on collectedsensor data. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor a pipelinesetting and having a backend system that includes an analytics modulethat performs analytics tasks on sensor data received from the sensorkit. In embodiments, provided herein is a sensor kit having sensors andan edge device that are configured to monitor a pipeline setting andhaving a backend system that includes a control module that providescommands to a device or system in an industrial setting to take remedialaction in response to a particular issue being detected. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a pipeline setting and having a backend systemthat includes a dashboard module that presents a dashboard to a humanuser that provides the human user with raw sensor data, analytical data,and/or predictions or classifications based on sensor data received fromthe sensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor a pipelinesetting and having a backend system that includes a dashboard modulethat presents a dashboard to a human user that provides a graphical userinterface that allows the user to configure the sensor kit system. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a pipeline setting and having asensor kit and a backend system that includes a configuration modulethat maintains configurations of the sensor kit and configures a sensorkit network by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a pipeline setting and having a sensor kit anda backend system that updates a distributed ledger based on sensor dataprovided by the sensor kit. In embodiments, provided herein is a sensorkit having sensors and an edge device that are configured to monitor apipeline setting and having a sensor kit and a backend system thatupdates a smart contract defining a condition that may trigger an actionbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a pipeline setting and having a distributedledger that is at least partially shared with a regulatory body toprovide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor apipeline setting and having sensor kit and a backend system that updatesa smart contract, wherein the smart contract verifies one or moreconditions put forth by a regulatory body with respect to compliancewith a regulation or regulatory action. In embodiments, provided hereinis a sensor kit having sensors and an edge device that are configured tomonitor a pipeline setting and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a manufacturing facility. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a manufacturing facility andhaving sensors and an edge device that are configured to monitor anunderwater industrial setting. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor a manufacturing facility and having a sensor kit that collectssensor data and a backend system that receives the sensor data from thesensor kits and updates a distributed ledger based on the sensor data.In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor a manufacturing facility andhaving sensors and an edge device that is configured to add new sensorsto the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor amanufacturing facility and having sensors, an edge device, and a gatewaydevice that communicates with a communication network on behalf of thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor amanufacturing facility and having an edge device that includes a dataprocessing module that deduplicates, filters, flags, and/or aggregatessensor data. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor amanufacturing facility and having an edge device that includes anencoding module that encodes, compresses, and/or encrypts sensor dataaccording to one or more media codecs. In embodiments, provided hereinis a sensor kit having sensors and an edge device that are configured tomonitor a manufacturing facility and having an edge device that includesa quick-decision AI module that uses machine-learned models to generatepredictions related to and/or classifications of industrial componentsbased on features of collected sensor data. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor a manufacturing facility and having an edge devicethat includes a notification module that provides notifications and/oralarms to users based on sensor data and/or rules applied to the sensordata. In embodiments, provided herein is a sensor kit having sensors andan edge device that are configured to monitor a manufacturing facilityand having an edge device that includes a configuration module thatconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a manufacturing facility andhaving an edge device that includes a distributed ledger moduleconfigured to update a distributed ledger with sensor data captured bythe sensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor amanufacturing facility and having a backend system that includes adecoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor amanufacturing facility and having a backend system that includes a dataprocessing module that executes a workflow associated with a potentialissue based on sensor data captured by the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a manufacturing facility and having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor amanufacturing facility and having a backend system that includes anotification module that issues notifications to users when an issue isdetected in an industrial setting based on collected sensor data. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a manufacturing facility andhaving a backend system that includes an analytics module that performsanalytics tasks on sensor data received from the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor a manufacturing facility andhaving a backend system that includes a control module that providescommands to a device or system in an industrial setting to take remedialaction in response to a particular issue being detected. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a manufacturing facility and having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides the human user with raw sensor data, analyticaldata, and/or predictions or classifications based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor a manufacturing facility and having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides a graphical user interface that allows the user toconfigure the sensor kit system. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor a manufacturing facility and having a sensor kit and a backendsystem that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor a manufacturing facility and having a sensor kit and a backendsystem that updates a distributed ledger based on sensor data providedby the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor amanufacturing facility and having a sensor kit and a backend system thatupdates a smart contract defining a condition that may trigger an actionbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a manufacturing facility and having adistributed ledger that is at least partially shared with a regulatorybody to provide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor amanufacturing facility and having sensor kit and a backend system thatupdates a smart contract, wherein the smart contract verifies one ormore conditions put forth by a regulatory body with respect tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor a manufacturing facility and having a sensor,an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that are configured to monitor an underwater industrialsetting. In embodiments, provided herein is a sensor kit having sensorsand an edge device that are configured to monitor an underwaterindustrial setting and having a sensor kit that collects sensor data anda backend system that receives the sensor data from the sensor kits andupdates a distributed ledger based on the sensor data. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an underwater industrial setting and havingsensors and an edge device that is configured to add new sensors to thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor an underwaterindustrial setting and having sensors, an edge device, and a gatewaydevice that communicates with a communication network on behalf of thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor an underwaterindustrial setting and having an edge device that includes a dataprocessing module that deduplicates, filters, flags, and/or aggregatessensor data. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor an underwaterindustrial setting and having an edge device that includes an encodingmodule that encodes, compresses, and/or encrypts sensor data accordingto one or more media codecs. In embodiments, provided herein is a sensorkit having sensors and an edge device that are configured to monitor anunderwater industrial setting and having an edge device that includes aquick-decision AI module that uses machine-learned models to generatepredictions related to and/or classifications of industrial componentsbased on features of collected sensor data. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an underwater industrial setting and having anedge device that includes a notification module that providesnotifications and/or alarms to users based on sensor data and/or rulesapplied to the sensor data. In embodiments, provided herein is a sensorkit having sensors and an edge device that are configured to monitor anunderwater industrial setting and having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that are configured tomonitor an underwater industrial setting and having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an underwater industrial setting and having abackend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an underwater industrial setting and having abackend system that includes a data processing module that executes aworkflow associated with a potential issue based on sensor data capturedby the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anunderwater industrial setting and having a backend system that includesan AI module that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that are configured to monitor an underwater industrial settingand having a backend system that includes a notification module thatissues notifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having sensors and an edge device that are configured tomonitor an underwater industrial setting and having a backend systemthat includes an analytics module that performs analytics tasks onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an underwater industrial setting and having abackend system that includes a control module that provides commands toa device or system in an industrial setting to take remedial action inresponse to a particular issue being detected. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an underwater industrial setting and having abackend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an underwater industrial setting and having abackend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an underwater industrial setting and having asensor kit and a backend system that includes a configuration modulethat maintains configurations of the sensor kit and configures a sensorkit network by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an underwater industrial setting and having asensor kit and a backend system that updates a distributed ledger basedon sensor data provided by the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that areconfigured to monitor an underwater industrial setting and having asensor kit and a backend system that updates a smart contract defining acondition that may trigger an action based on sensor data received fromthe sensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that are configured to monitor an underwaterindustrial setting and having a distributed ledger that is at leastpartially shared with a regulatory body to provide information relatedto compliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having sensors and an edge device thatare configured to monitor an underwater industrial setting and havingsensor kit and a backend system that updates a smart contract, whereinthe smart contract verifies one or more conditions put forth by aregulatory body with respect to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that are configured to monitor anunderwater industrial setting and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit system having a sensorkit that collects sensor data and a backend system that receives thesensor data from the sensor kits and updates a distributed ledger basedon the sensor data. In embodiments, provided herein is a sensor kitsystem having a sensor kit that collects sensor data and a backendsystem that receives the sensor data from the sensor kits and updates adistributed ledger based on the sensor data and having sensors and anedge device that is configured to add new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a sensor kitthat collects sensor data and a backend system that receives the sensordata from the sensor kits and updates a distributed ledger based on thesensor data and having sensors, an edge device, and a gateway devicethat communicates with a communication network on behalf of the sensorkit. In embodiments, provided herein is a sensor kit system having asensor kit that collects sensor data and a backend system that receivesthe sensor data from the sensor kits and updates a distributed ledgerbased on the sensor data and having an edge device that includes a dataprocessing module that deduplicates, filters, flags, and/or aggregatessensor data. In embodiments, provided herein is a sensor kit systemhaving a sensor kit that collects sensor data and a backend system thatreceives the sensor data from the sensor kits and updates a distributedledger based on the sensor data and having an edge device that includesan encoding module that encodes, compresses, and/or encrypts sensor dataaccording to one or more media codecs. In embodiments, provided hereinis a sensor kit system having a sensor kit that collects sensor data anda backend system that receives the sensor data from the sensor kits andupdates a distributed ledger based on the sensor data and having an edgedevice that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data. In embodiments, provided herein is a sensor kit systemhaving a sensor kit that collects sensor data and a backend system thatreceives the sensor data from the sensor kits and updates a distributedledger based on the sensor data and having an edge device that includesa notification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data. Inembodiments, provided herein is a sensor kit system having a sensor kitthat collects sensor data and a backend system that receives the sensordata from the sensor kits and updates a distributed ledger based on thesensor data and having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein is a sensorkit system having a sensor kit that collects sensor data and a backendsystem that receives the sensor data from the sensor kits and updates adistributed ledger based on the sensor data and having an edge devicethat includes a distributed ledger module configured to update adistributed ledger with sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit system having a sensor kitthat collects sensor data and a backend system that receives the sensordata from the sensor kits and updates a distributed ledger based on thesensor data and having a backend system that includes a decoding modulethat decrypts, decodes, and/or decompresses encoded sensor kit packets.In embodiments, provided herein is a sensor kit system having a sensorkit that collects sensor data and a backend system that receives thesensor data from the sensor kits and updates a distributed ledger basedon the sensor data and having a backend system that includes a dataprocessing module that executes a workflow associated with a potentialissue based on sensor data captured by the sensor kit. In embodiments,provided herein is a sensor kit system having a sensor kit that collectssensor data and a backend system that receives the sensor data from thesensor kits and updates a distributed ledger based on the sensor dataand having a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit. In embodiments, provided herein isa sensor kit system having a sensor kit that collects sensor data and abackend system that receives the sensor data from the sensor kits andupdates a distributed ledger based on the sensor data and having abackend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit system having a sensor kit that collects sensor data anda backend system that receives the sensor data from the sensor kits andupdates a distributed ledger based on the sensor data and having abackend system that includes an analytics module that performs analyticstasks on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit system having a sensor kit that collectssensor data and a backend system that receives the sensor data from thesensor kits and updates a distributed ledger based on the sensor dataand having a backend system that includes a control module that providescommands to a device or system in an industrial setting to take remedialaction in response to a particular issue being detected. In embodiments,provided herein is a sensor kit system having a sensor kit that collectssensor data and a backend system that receives the sensor data from thesensor kits and updates a distributed ledger based on the sensor dataand having a backend system that includes a dashboard module thatpresents a dashboard to a human user that provides the human user withraw sensor data, analytical data, and/or predictions or classificationsbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit system having a sensor kit that collectssensor data and a backend system that receives the sensor data from thesensor kits and updates a distributed ledger based on the sensor dataand having a backend system that includes a dashboard module thatpresents a dashboard to a human user that provides a graphical userinterface that allows the user to configure the sensor kit system. Inembodiments, provided herein is a sensor kit system having a sensor kitthat collects sensor data and a backend system that receives the sensordata from the sensor kits and updates a distributed ledger based on thesensor data and having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a sensor kitthat collects sensor data and a backend system that receives the sensordata from the sensor kits and updates a distributed ledger based on thesensor data and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit system having a sensor kitthat collects sensor data and a backend system that receives the sensordata from the sensor kits and updates a distributed ledger based on thesensor data and having a sensor kit and a backend system that updates asmart contract defining a condition that may trigger an action based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a sensor kit that collects sensordata and a backend system that receives the sensor data from the sensorkits and updates a distributed ledger based on the sensor data andhaving a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein is asensor kit system having a sensor kit that collects sensor data and abackend system that receives the sensor data from the sensor kits andupdates a distributed ledger based on the sensor data and having sensorkit and a backend system that updates a smart contract, wherein thesmart contract verifies one or more conditions put forth by a regulatorybody with respect to compliance with a regulation or regulatory action.In embodiments, provided herein is a sensor kit system having a sensorkit that collects sensor data and a backend system that receives thesensor data from the sensor kits and updates a distributed ledger basedon the sensor data and having a sensor, an edge device, and a gatewaydevice that communicates with a communication network on behalf of thesensor kit.

In embodiments, provided herein is a sensor kit having sensors and anedge device that is configured to add new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that is configured to add new sensors to the sensor kit andhaving sensors, an edge device, and a gateway device that communicateswith a communication network on behalf of the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that is configured to add new sensors to the sensor kit andhaving an edge device that includes a data processing module thatdeduplicates, filters, flags, and/or aggregates sensor data. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that is configured to add new sensors to the sensor kit andhaving an edge device that includes an encoding module that encodes,compresses, and/or encrypts sensor data according to one or more mediacodecs. In embodiments, provided herein is a sensor kit having sensorsand an edge device that is configured to add new sensors to the sensorkit and having an edge device that includes a quick-decision AI modulethat uses machine-learned models to generate predictions related toand/or classifications of industrial components based on features ofcollected sensor data. In embodiments, provided herein is a sensor kithaving sensors and an edge device that is configured to add new sensorsto the sensor kit and having an edge device that includes a notificationmodule that provides notifications and/or alarms to users based onsensor data and/or rules applied to the sensor data. In embodiments,provided herein is a sensor kit having sensors and an edge device thatis configured to add new sensors to the sensor kit and having an edgedevice that includes a configuration module that configures a sensor kitnetwork by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatis configured to add new sensors to the sensor kit and having an edgedevice that includes a distributed ledger module configured to update adistributed ledger with sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit having sensors and an edgedevice that is configured to add new sensors to the sensor kit andhaving a backend system that includes a decoding module that decrypts,decodes, and/or decompresses encoded sensor kit packets. In embodiments,provided herein is a sensor kit having sensors and an edge device thatis configured to add new sensors to the sensor kit and having a backendsystem that includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that is configured to add new sensors to thesensor kit and having a backend system that includes an AI module thattrains machine-learned models to make predictions or classificationsrelated to sensor data captured by a sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatis configured to add new sensors to the sensor kit and having a backendsystem that includes a notification module that issues notifications tousers when an issue is detected in an industrial setting based oncollected sensor data. In embodiments, provided herein is a sensor kithaving sensors and an edge device that is configured to add new sensorsto the sensor kit and having a backend system that includes an analyticsmodule that performs analytics tasks on sensor data received from thesensor kit. In embodiments, provided herein is a sensor kit havingsensors and an edge device that is configured to add new sensors to thesensor kit and having a backend system that includes a control modulethat provides commands to a device or system in an industrial setting totake remedial action in response to a particular issue being detected.In embodiments, provided herein is a sensor kit having sensors and anedge device that is configured to add new sensors to the sensor kit andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having sensors and an edge device that isconfigured to add new sensors to the sensor kit and having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides a graphical user interface that allows the userto configure the sensor kit system. In embodiments, provided herein is asensor kit having sensors and an edge device that is configured to addnew sensors to the sensor kit and having a sensor kit and a backendsystem that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors and an edge device that is configured to addnew sensors to the sensor kit and having a sensor kit and a backendsystem that updates a distributed ledger based on sensor data providedby the sensor kit. In embodiments, provided herein is a sensor kithaving sensors and an edge device that is configured to add new sensorsto the sensor kit and having a sensor kit and a backend system thatupdates a smart contract defining a condition that may trigger an actionbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having sensors and an edge device thatis configured to add new sensors to the sensor kit and having adistributed ledger that is at least partially shared with a regulatorybody to provide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving sensors and an edge device that is configured to add new sensorsto the sensor kit and having sensor kit and a backend system thatupdates a smart contract, wherein the smart contract verifies one ormore conditions put forth by a regulatory body with respect tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having sensors and an edge device thatis configured to add new sensors to the sensor kit and having a sensor,an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having sensors, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit. In embodiments, provided herein isa sensor kit having sensors, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kitand having an edge device that includes a data processing module thatdeduplicates, filters, flags, and/or aggregates sensor data. Inembodiments, provided herein is a sensor kit having sensors, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit and having an edge device thatincludes an encoding module that encodes, compresses, and/or encryptssensor data according to one or more media codecs. In embodiments,provided herein is a sensor kit having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit and having an edge device that includes aquick-decision AI module that uses machine-learned models to generatepredictions related to and/or classifications of industrial componentsbased on features of collected sensor data. In embodiments, providedherein is a sensor kit having sensors, an edge device, and a gatewaydevice that communicates with a communication network on behalf of thesensor kit and having an edge device that includes a notification modulethat provides notifications and/or alarms to users based on sensor dataand/or rules applied to the sensor data. In embodiments, provided hereinis a sensor kit having sensors, an edge device, and a gateway devicethat communicates with a communication network on behalf of the sensorkit and having an edge device that includes a configuration module thatconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having sensors, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit and having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit. In embodiments,provided herein is a sensor kit having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit and having a backend system that includes a decodingmodule that decrypts, decodes, and/or decompresses encoded sensor kitpackets. In embodiments, provided herein is a sensor kit having sensors,an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit and having a backendsystem that includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit. In embodiments, provided herein is a sensor kit havingsensors, an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit and having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein is a sensor kit havingsensors, an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit and having a backendsystem that includes a notification module that issues notifications tousers when an issue is detected in an industrial setting based oncollected sensor data. In embodiments, provided herein is a sensor kithaving sensors, an edge device, and a gateway device that communicateswith a communication network on behalf of the sensor kit and having abackend system that includes an analytics module that performs analyticstasks on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit and having a backend system that includes a controlmodule that provides commands to a device or system in an industrialsetting to take remedial action in response to a particular issue beingdetected. In embodiments, provided herein is a sensor kit havingsensors, an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit and having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides the human user with raw sensor data, analyticaldata, and/or predictions or classifications based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit having sensors, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kitand having a backend system that includes a dashboard module thatpresents a dashboard to a human user that provides a graphical userinterface that allows the user to configure the sensor kit system. Inembodiments, provided herein is a sensor kit having sensors, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit and having a sensor kit and abackend system that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having sensors, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kitand having a sensor kit and a backend system that updates a distributedledger based on sensor data provided by the sensor kit. In embodiments,provided herein is a sensor kit having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit and having a sensor kit and a backend system thatupdates a smart contract defining a condition that may trigger an actionbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit and having a distributed ledger that is at leastpartially shared with a regulatory body to provide information relatedto compliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit and having sensor kit and a backend system thatupdates a smart contract, wherein the smart contract verifies one ormore conditions put forth by a regulatory body with respect tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having sensors, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit and having a sensor, an edge device, and a gatewaydevice that communicates with a communication network on behalf of thesensor kit.

In embodiments, provided herein is a sensor kit having an edge devicethat includes a data processing module that deduplicates, filters,flags, and/or aggregates sensor data. In embodiments, provided herein isa sensor kit having an edge device that includes a data processingmodule that deduplicates, filters, flags, and/or aggregates sensor dataand having an edge device that includes an encoding module that encodes,compresses, and/or encrypts sensor data according to one or more mediacodecs. In embodiments, provided herein is a sensor kit having an edgedevice that includes a data processing module that deduplicates,filters, flags, and/or aggregates sensor data and having an edge devicethat includes a quick-decision AI module that uses machine-learnedmodels to generate predictions related to and/or classifications ofindustrial components based on features of collected sensor data. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a data processing module that deduplicates, filters, flags,and/or aggregates sensor data and having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a data processing module that deduplicates, filters, flags,and/or aggregates sensor data and having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having an edge device that includes a data processing modulethat deduplicates, filters, flags, and/or aggregates sensor data andhaving an edge device that includes a distributed ledger moduleconfigured to update a distributed ledger with sensor data captured bythe sensor kit. In embodiments, provided herein is a sensor kit havingan edge device that includes a data processing module that deduplicates,filters, flags, and/or aggregates sensor data and having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets. In embodiments, provided hereinis a sensor kit having an edge device that includes a data processingmodule that deduplicates, filters, flags, and/or aggregates sensor dataand having a backend system that includes a data processing module thatexecutes a workflow associated with a potential issue based on sensordata captured by the sensor kit. In embodiments, provided herein is asensor kit having an edge device that includes a data processing modulethat deduplicates, filters, flags, and/or aggregates sensor data andhaving a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit. In embodiments, provided herein isa sensor kit having an edge device that includes a data processingmodule that deduplicates, filters, flags, and/or aggregates sensor dataand having a backend system that includes a notification module thatissues notifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having an edge device that includes a data processingmodule that deduplicates, filters, flags, and/or aggregates sensor dataand having a backend system that includes an analytics module thatperforms analytics tasks on sensor data received from the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a data processing module that deduplicates, filters, flags,and/or aggregates sensor data and having a backend system that includesa control module that provides commands to a device or system in anindustrial setting to take remedial action in response to a particularissue being detected. In embodiments, provided herein is a sensor kithaving an edge device that includes a data processing module thatdeduplicates, filters, flags, and/or aggregates sensor data and having abackend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having an edge device that includes a dataprocessing module that deduplicates, filters, flags, and/or aggregatessensor data and having a backend system that includes a dashboard modulethat presents a dashboard to a human user that provides a graphical userinterface that allows the user to configure the sensor kit system. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a data processing module that deduplicates, filters, flags,and/or aggregates sensor data and having a sensor kit and a backendsystem that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having an edge device that includes a data processing modulethat deduplicates, filters, flags, and/or aggregates sensor data andhaving a sensor kit and a backend system that updates a distributedledger based on sensor data provided by the sensor kit. In embodiments,provided herein is a sensor kit having an edge device that includes adata processing module that deduplicates, filters, flags, and/oraggregates sensor data and having a sensor kit and a backend system thatupdates a smart contract defining a condition that may trigger an actionbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having an edge device that includes adata processing module that deduplicates, filters, flags, and/oraggregates sensor data and having a distributed ledger that is at leastpartially shared with a regulatory body to provide information relatedto compliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having an edge device that includes adata processing module that deduplicates, filters, flags, and/oraggregates sensor data and having sensor kit and a backend system thatupdates a smart contract, wherein the smart contract verifies one ormore conditions put forth by a regulatory body with respect tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having an edge device that includes adata processing module that deduplicates, filters, flags, and/oraggregates sensor data and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit having an edge devicethat includes an encoding module that encodes, compresses, and/orencrypts sensor data according to one or more media codecs. Inembodiments, provided herein is a sensor kit having an edge device thatincludes an encoding module that encodes, compresses, and/or encryptssensor data according to one or more media codecs and having an edgedevice that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data. In embodiments, provided herein is a sensor kit having anedge device that includes an encoding module that encodes, compresses,and/or encrypts sensor data according to one or more media codecs andhaving an edge device that includes a notification module that providesnotifications and/or alarms to users based on sensor data and/or rulesapplied to the sensor data. In embodiments, provided herein is a sensorkit having an edge device that includes an encoding module that encodes,compresses, and/or encrypts sensor data according to one or more mediacodecs and having an edge device that includes a configuration modulethat configures a sensor kit network by transmitting configurationrequests to sensor devices, generating device records based on responsesto the configuration requests, and/or adding new sensors to the sensorkit. In embodiments, provided herein is a sensor kit having an edgedevice that includes an encoding module that encodes, compresses, and/orencrypts sensor data according to one or more media codecs and having anedge device that includes a distributed ledger module configured toupdate a distributed ledger with sensor data captured by the sensor kit.In embodiments, provided herein is a sensor kit having an edge devicethat includes an encoding module that encodes, compresses, and/orencrypts sensor data according to one or more media codecs and having abackend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets. In embodiments, providedherein is a sensor kit having an edge device that includes an encodingmodule that encodes, compresses, and/or encrypts sensor data accordingto one or more media codecs and having a backend system that includes adata processing module that executes a workflow associated with apotential issue based on sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes an encoding module that encodes, compresses, and/or encryptssensor data according to one or more media codecs and having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein is a sensor kit having anedge device that includes an encoding module that encodes, compresses,and/or encrypts sensor data according to one or more media codecs andhaving a backend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having an edge device that includes an encoding modulethat encodes, compresses, and/or encrypts sensor data according to oneor more media codecs and having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit. In embodiments, provided herein is a sensor kithaving an edge device that includes an encoding module that encodes,compresses, and/or encrypts sensor data according to one or more mediacodecs and having a backend system that includes a control module thatprovides commands to a device or system in an industrial setting to takeremedial action in response to a particular issue being detected. Inembodiments, provided herein is a sensor kit having an edge device thatincludes an encoding module that encodes, compresses, and/or encryptssensor data according to one or more media codecs and having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides the human user with raw sensor data, analyticaldata, and/or predictions or classifications based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit having an edge device that includes an encoding module thatencodes, compresses, and/or encrypts sensor data according to one ormore media codecs and having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides agraphical user interface that allows the user to configure the sensorkit system. In embodiments, provided herein is a sensor kit having anedge device that includes an encoding module that encodes, compresses,and/or encrypts sensor data according to one or more media codecs andhaving a sensor kit and a backend system that includes a configurationmodule that maintains configurations of the sensor kit and configures asensor kit network by transmitting configuration requests to sensordevices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes an encoding module that encodes, compresses, and/or encryptssensor data according to one or more media codecs and having a sensorkit and a backend system that updates a distributed ledger based onsensor data provided by the sensor kit. In embodiments, provided hereinis a sensor kit having an edge device that includes an encoding modulethat encodes, compresses, and/or encrypts sensor data according to oneor more media codecs and having a sensor kit and a backend system thatupdates a smart contract defining a condition that may trigger an actionbased on sensor data received from the sensor kit. In embodiments,provided herein is a sensor kit having an edge device that includes anencoding module that encodes, compresses, and/or encrypts sensor dataaccording to one or more media codecs and having a distributed ledgerthat is at least partially shared with a regulatory body to provideinformation related to compliance with a regulation or regulatoryaction. In embodiments, provided herein is a sensor kit having an edgedevice that includes an encoding module that encodes, compresses, and/orencrypts sensor data according to one or more media codecs and havingsensor kit and a backend system that updates a smart contract, whereinthe smart contract verifies one or more conditions put forth by aregulatory body with respect to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving an edge device that includes an encoding module that encodes,compresses, and/or encrypts sensor data according to one or more mediacodecs and having a sensor, an edge device, and a gateway device thatcommunicates with a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having an edge devicethat includes a quick-decision AI module that uses machine-learnedmodels to generate predictions related to and/or classifications ofindustrial components based on features of collected sensor data. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data and having an edgedevice that includes a notification module that provides notificationsand/or alarms to users based on sensor data and/or rules applied to thesensor data. In embodiments, provided herein is a sensor kit having anedge device that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data and having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein is a sensorkit having an edge device that includes a quick-decision AI module thatuses machine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data and having an edge device that includes a distributed ledgermodule configured to update a distributed ledger with sensor datacaptured by the sensor kit. In embodiments, provided herein is a sensorkit having an edge device that includes a quick-decision AI module thatuses machine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data and having a backend system that includes a decoding modulethat decrypts, decodes, and/or decompresses encoded sensor kit packets.In embodiments, provided herein is a sensor kit having an edge devicethat includes a quick-decision AI module that uses machine-learnedmodels to generate predictions related to and/or classifications ofindustrial components based on features of collected sensor data andhaving a backend system that includes a data processing module thatexecutes a workflow associated with a potential issue based on sensordata captured by the sensor kit. In embodiments, provided herein is asensor kit having an edge device that includes a quick-decision AImodule that uses machine-learned models to generate predictions relatedto and/or classifications of industrial components based on features ofcollected sensor data and having a backend system that includes an AImodule that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data and having abackend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having an edge device that includes a quick-decision AImodule that uses machine-learned models to generate predictions relatedto and/or classifications of industrial components based on features ofcollected sensor data and having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit. In embodiments, provided herein is a sensor kithaving an edge device that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data and having a backend system that includes a control modulethat provides commands to a device or system in an industrial setting totake remedial action in response to a particular issue being detected.In embodiments, provided herein is a sensor kit having an edge devicethat includes a quick-decision AI module that uses machine-learnedmodels to generate predictions related to and/or classifications ofindustrial components based on features of collected sensor data andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having an edge device that includes aquick-decision AI module that uses machine-learned models to generatepredictions related to and/or classifications of industrial componentsbased on features of collected sensor data and having a backend systemthat includes a dashboard module that presents a dashboard to a humanuser that provides a graphical user interface that allows the user toconfigure the sensor kit system. In embodiments, provided herein is asensor kit having an edge device that includes a quick-decision AImodule that uses machine-learned models to generate predictions relatedto and/or classifications of industrial components based on features ofcollected sensor data and having a sensor kit and a backend system thatincludes a configuration module that maintains configurations of thesensor kit and configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein is a sensorkit having an edge device that includes a quick-decision AI module thatuses machine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data and having asensor kit and a backend system that updates a smart contract defining acondition that may trigger an action based on sensor data received fromthe sensor kit. In embodiments, provided herein is a sensor kit havingan edge device that includes a quick-decision AI module that usesmachine-learned models to generate predictions related to and/orclassifications of industrial components based on features of collectedsensor data and having a distributed ledger that is at least partiallyshared with a regulatory body to provide information related tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having an edge device that includes aquick-decision AI module that uses machine-learned models to generatepredictions related to and/or classifications of industrial componentsbased on features of collected sensor data and having sensor kit and abackend system that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a quick-decision AI module that uses machine-learned models togenerate predictions related to and/or classifications of industrialcomponents based on features of collected sensor data and having asensor, an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having an edge devicethat includes a notification module that provides notifications and/oralarms to users based on sensor data and/or rules applied to the sensordata. In embodiments, provided herein is a sensor kit having an edgedevice that includes a notification module that provides notificationsand/or alarms to users based on sensor data and/or rules applied to thesensor data and having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein is a sensorkit having an edge device that includes a notification module thatprovides notifications and/or alarms to users based on sensor dataand/or rules applied to the sensor data and having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit. In embodiments,provided herein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga backend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets. In embodiments, providedherein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga backend system that includes a data processing module that executes aworkflow associated with a potential issue based on sensor data capturedby the sensor kit. In embodiments, provided herein is a sensor kithaving an edge device that includes a notification module that providesnotifications and/or alarms to users based on sensor data and/or rulesapplied to the sensor data and having a backend system that includes anAI module that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a notification module that provides notifications and/or alarmsto users based on sensor data and/or rules applied to the sensor dataand having a backend system that includes a notification module thatissues notifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having an edge device that includes a notificationmodule that provides notifications and/or alarms to users based onsensor data and/or rules applied to the sensor data and having a backendsystem that includes an analytics module that performs analytics taskson sensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga backend system that includes a control module that provides commandsto a device or system in an industrial setting to take remedial actionin response to a particular issue being detected. In embodiments,provided herein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga backend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga backend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga sensor kit and a backend system that includes a configuration modulethat maintains configurations of the sensor kit and configures a sensorkit network by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga sensor kit and a backend system that updates a distributed ledgerbased on sensor data provided by the sensor kit. In embodiments,provided herein is a sensor kit having an edge device that includes anotification module that provides notifications and/or alarms to usersbased on sensor data and/or rules applied to the sensor data and havinga sensor kit and a backend system that updates a smart contract defininga condition that may trigger an action based on sensor data receivedfrom the sensor kit. In embodiments, provided herein is a sensor kithaving an edge device that includes a notification module that providesnotifications and/or alarms to users based on sensor data and/or rulesapplied to the sensor data and having a distributed ledger that is atleast partially shared with a regulatory body to provide informationrelated to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a notification module that provides notifications and/or alarmsto users based on sensor data and/or rules applied to the sensor dataand having sensor kit and a backend system that updates a smartcontract, wherein the smart contract verifies one or more conditions putforth by a regulatory body with respect to compliance with a regulationor regulatory action. In embodiments, provided herein is a sensor kithaving an edge device that includes a notification module that providesnotifications and/or alarms to users based on sensor data and/or rulesapplied to the sensor data and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit having an edge devicethat includes a configuration module that configures a sensor kitnetwork by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having an edge device that includes adistributed ledger module configured to update a distributed ledger withsensor data captured by the sensor kit. In embodiments, provided hereinis a sensor kit having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit and having a backend system that includes adecoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets. In embodiments, provided herein is a sensor kithaving an edge device that includes a configuration module thatconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit andhaving a backend system that includes a data processing module thatexecutes a workflow associated with a potential issue based on sensordata captured by the sensor kit. In embodiments, provided herein is asensor kit having an edge device that includes a configuration modulethat configures a sensor kit network by transmitting configurationrequests to sensor devices, generating device records based on responsesto the configuration requests, and/or adding new sensors to the sensorkit and having a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit. In embodiments, provided herein isa sensor kit having an edge device that includes a configuration modulethat configures a sensor kit network by transmitting configurationrequests to sensor devices, generating device records based on responsesto the configuration requests, and/or adding new sensors to the sensorkit and having a backend system that includes a notification module thatissues notifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having an edge device that includes a configurationmodule that configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit and having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit. In embodiments, provided herein is a sensor kithaving an edge device that includes a configuration module thatconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit andhaving a backend system that includes a control module that providescommands to a device or system in an industrial setting to take remedialaction in response to a particular issue being detected. In embodiments,provided herein is a sensor kit having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having a backend system that includesa dashboard module that presents a dashboard to a human user thatprovides the human user with raw sensor data, analytical data, and/orpredictions or classifications based on sensor data received from thesensor kit. In embodiments, provided herein is a sensor kit having anedge device that includes a configuration module that configures asensor kit network by transmitting configuration requests to sensordevices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having a sensor kit and a backendsystem that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit having an edge device that includes a configuration modulethat configures a sensor kit network by transmitting configurationrequests to sensor devices, generating device records based on responsesto the configuration requests, and/or adding new sensors to the sensorkit and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a configuration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having a sensor kit and a backendsystem that updates a smart contract defining a condition that maytrigger an action based on sensor data received from the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a configuration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having a distributed ledger that is atleast partially shared with a regulatory body to provide informationrelated to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a configuration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having sensor kit and a backend systemthat updates a smart contract, wherein the smart contract verifies oneor more conditions put forth by a regulatory body with respect tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit having an edge device that includes aconfiguration module that configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit having an edge devicethat includes a distributed ledger module configured to update adistributed ledger with sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit and having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets. In embodiments, provided hereinis a sensor kit having an edge device that includes a distributed ledgermodule configured to update a distributed ledger with sensor datacaptured by the sensor kit and having a backend system that includes adata processing module that executes a workflow associated with apotential issue based on sensor data captured by the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit and having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein is a sensor kit having anedge device that includes a distributed ledger module configured toupdate a distributed ledger with sensor data captured by the sensor kitand having a backend system that includes a notification module thatissues notifications to users when an issue is detected in an industrialsetting based on collected sensor data. In embodiments, provided hereinis a sensor kit having an edge device that includes a distributed ledgermodule configured to update a distributed ledger with sensor datacaptured by the sensor kit and having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit. In embodiments, provided herein is a sensor kithaving an edge device that includes a distributed ledger moduleconfigured to update a distributed ledger with sensor data captured bythe sensor kit and having a backend system that includes a controlmodule that provides commands to a device or system in an industrialsetting to take remedial action in response to a particular issue beingdetected. In embodiments, provided herein is a sensor kit having an edgedevice that includes a distributed ledger module configured to update adistributed ledger with sensor data captured by the sensor kit andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit having an edge device that includes a distributedledger module configured to update a distributed ledger with sensor datacaptured by the sensor kit and having a backend system that includes adashboard module that presents a dashboard to a human user that providesa graphical user interface that allows the user to configure the sensorkit system. In embodiments, provided herein is a sensor kit having anedge device that includes a distributed ledger module configured toupdate a distributed ledger with sensor data captured by the sensor kitand having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit and having a sensorkit and a backend system that updates a distributed ledger based onsensor data provided by the sensor kit. In embodiments, provided hereinis a sensor kit having an edge device that includes a distributed ledgermodule configured to update a distributed ledger with sensor datacaptured by the sensor kit and having a sensor kit and a backend systemthat updates a smart contract defining a condition that may trigger anaction based on sensor data received from the sensor kit. Inembodiments, provided herein is a sensor kit having an edge device thatincludes a distributed ledger module configured to update a distributedledger with sensor data captured by the sensor kit and having adistributed ledger that is at least partially shared with a regulatorybody to provide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kithaving an edge device that includes a distributed ledger moduleconfigured to update a distributed ledger with sensor data captured bythe sensor kit and having sensor kit and a backend system that updates asmart contract, wherein the smart contract verifies one or moreconditions put forth by a regulatory body with respect to compliancewith a regulation or regulatory action. In embodiments, provided hereinis a sensor kit having an edge device that includes a distributed ledgermodule configured to update a distributed ledger with sensor datacaptured by the sensor kit and having a sensor, an edge device, and agateway device that communicates with a communication network on behalfof the sensor kit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets. In embodiments, provided hereinis a sensor kit system having a backend system that includes a decodingmodule that decrypts, decodes, and/or decompresses encoded sensor kitpackets and having a backend system that includes a data processingmodule that executes a workflow associated with a potential issue basedon sensor data captured by the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes adecoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets and having a backend system that includes an AImodule that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets and having a backend system thatincludes a notification module that issues notifications to users whenan issue is detected in an industrial setting based on collected sensordata. In embodiments, provided herein is a sensor kit system having abackend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets and having a backendsystem that includes an analytics module that performs analytics taskson sensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes adecoding module that decrypts, decodes, and/or decompresses encodedsensor kit packets and having a backend system that includes a controlmodule that provides commands to a device or system in an industrialsetting to take remedial action in response to a particular issue beingdetected. In embodiments, provided herein is a sensor kit system havinga backend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets and having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides the human user with raw sensor data, analyticaldata, and/or predictions or classifications based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes a decodingmodule that decrypts, decodes, and/or decompresses encoded sensor kitpackets and having a backend system that includes a dashboard modulethat presents a dashboard to a human user that provides a graphical userinterface that allows the user to configure the sensor kit system. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets and having a sensor kit and abackend system that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes a decodingmodule that decrypts, decodes, and/or decompresses encoded sensor kitpackets and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets and having a sensor kit and abackend system that updates a smart contract defining a condition thatmay trigger an action based on sensor data received from the sensor kit.In embodiments, provided herein is a sensor kit system having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets and having a distributed ledgerthat is at least partially shared with a regulatory body to provideinformation related to compliance with a regulation or regulatoryaction. In embodiments, provided herein is a sensor kit system having abackend system that includes a decoding module that decrypts, decodes,and/or decompresses encoded sensor kit packets and having sensor kit anda backend system that updates a smart contract, wherein the smartcontract verifies one or more conditions put forth by a regulatory bodywith respect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a decoding module that decrypts, decodes, and/ordecompresses encoded sensor kit packets and having a sensor, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit. In embodiments, provided herein is a sensor kit systemhaving a backend system that includes a data processing module thatexecutes a workflow associated with a potential issue based on sensordata captured by the sensor kit and having a backend system thatincludes an AI module that trains machine-learned models to makepredictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein is a sensor kit systemhaving a backend system that includes a data processing module thatexecutes a workflow associated with a potential issue based on sensordata captured by the sensor kit and having a backend system thatincludes a notification module that issues notifications to users whenan issue is detected in an industrial setting based on collected sensordata. In embodiments, provided herein is a sensor kit system having abackend system that includes a data processing module that executes aworkflow associated with a potential issue based on sensor data capturedby the sensor kit and having a backend system that includes an analyticsmodule that performs analytics tasks on sensor data received from thesensor kit. In embodiments, provided herein is a sensor kit systemhaving a backend system that includes a data processing module thatexecutes a workflow associated with a potential issue based on sensordata captured by the sensor kit and having a backend system thatincludes a control module that provides commands to a device or systemin an industrial setting to take remedial action in response to aparticular issue being detected. In embodiments, provided herein is asensor kit system having a backend system that includes a dataprocessing module that executes a workflow associated with a potentialissue based on sensor data captured by the sensor kit and having abackend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes adata processing module that executes a workflow associated with apotential issue based on sensor data captured by the sensor kit andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit system having a backend system thatincludes a data processing module that executes a workflow associatedwith a potential issue based on sensor data captured by the sensor kitand having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit and having a sensor kit and a backend system that updates asmart contract defining a condition that may trigger an action based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes adata processing module that executes a workflow associated with apotential issue based on sensor data captured by the sensor kit andhaving a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein is asensor kit system having a backend system that includes a dataprocessing module that executes a workflow associated with a potentialissue based on sensor data captured by the sensor kit and having sensorkit and a backend system that updates a smart contract, wherein thesmart contract verifies one or more conditions put forth by a regulatorybody with respect to compliance with a regulation or regulatory action.In embodiments, provided herein is a sensor kit system having a backendsystem that includes a data processing module that executes a workflowassociated with a potential issue based on sensor data captured by thesensor kit and having a sensor, an edge device, and a gateway devicethat communicates with a communication network on behalf of the sensorkit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit. In embodiments, provided herein is a sensor kit systemhaving a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit and having a backend system thatincludes a notification module that issues notifications to users whenan issue is detected in an industrial setting based on collected sensordata. In embodiments, provided herein is a sensor kit system having abackend system that includes an AI module that trains machine-learnedmodels to make predictions or classifications related to sensor datacaptured by a sensor kit and having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit. In embodiments, provided herein is a sensor kitsystem having a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit and having a backend system thatincludes a control module that provides commands to a device or systemin an industrial setting to take remedial action in response to aparticular issue being detected. In embodiments, provided herein is asensor kit system having a backend system that includes an AI modulethat trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes anAI module that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit system having a backend system thatincludes an AI module that trains machine-learned models to makepredictions or classifications related to sensor data captured by asensor kit and having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit and having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes an AI module that trains machine-learned models tomake predictions or classifications related to sensor data captured by asensor kit and having a sensor kit and a backend system that updates asmart contract defining a condition that may trigger an action based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes anAI module that trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit andhaving a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein is asensor kit system having a backend system that includes an AI modulethat trains machine-learned models to make predictions orclassifications related to sensor data captured by a sensor kit andhaving sensor kit and a backend system that updates a smart contract,wherein the smart contract verifies one or more conditions put forth bya regulatory body with respect to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kitsystem having a backend system that includes an AI module that trainsmachine-learned models to make predictions or classifications related tosensor data captured by a sensor kit and having a sensor, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes a notification module that issues notifications tousers when an issue is detected in an industrial setting based oncollected sensor data. In embodiments, provided herein is a sensor kitsystem having a backend system that includes a notification module thatissues notifications to users when an issue is detected in an industrialsetting based on collected sensor data and having a backend system thatincludes an analytics module that performs analytics tasks on sensordata received from the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes a notificationmodule that issues notifications to users when an issue is detected inan industrial setting based on collected sensor data. and having abackend system that includes a control module that provides commands toa device or system in an industrial setting to take remedial action inresponse to a particular issue being detected. In embodiments, providedherein is a sensor kit system having a backend system that includes anotification module that issues notifications to users when an issue isdetected in an industrial setting based on collected sensor data. andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes anotification module that issues notifications to users when an issue isdetected in an industrial setting based on collected sensor data. andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit system having a backend system thatincludes a notification module that issues notifications to users whenan issue is detected in an industrial setting based on collected sensordata. and having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a notification module that issues notifications tousers when an issue is detected in an industrial setting based oncollected sensor data. and having a sensor kit and a backend system thatupdates a distributed ledger based on sensor data provided by the sensorkit. In embodiments, provided herein is a sensor kit system having abackend system that includes a notification module that issuesnotifications to users when an issue is detected in an industrialsetting based on collected sensor data. and having a sensor kit and abackend system that updates a smart contract defining a condition thatmay trigger an action based on sensor data received from the sensor kit.In embodiments, provided herein is a sensor kit system having a backendsystem that includes a notification module that issues notifications tousers when an issue is detected in an industrial setting based oncollected sensor data. and having a distributed ledger that is at leastpartially shared with a regulatory body to provide information relatedto compliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit system having a backend system thatincludes a notification module that issues notifications to users whenan issue is detected in an industrial setting based on collected sensordata. and having sensor kit and a backend system that updates a smartcontract, wherein the smart contract verifies one or more conditions putforth by a regulatory body with respect to compliance with a regulationor regulatory action. In embodiments, provided herein is a sensor kitsystem having a backend system that includes a notification module thatissues notifications to users when an issue is detected in an industrialsetting based on collected sensor data. and having a sensor, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes an analytics module that performs analytics taskson sensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit and having a backend system that includes a controlmodule that provides commands to a device or system in an industrialsetting to take remedial action in response to a particular issue beingdetected. In embodiments, provided herein is a sensor kit system havinga backend system that includes an analytics module that performsanalytics tasks on sensor data received from the sensor kit and having abackend system that includes a dashboard module that presents adashboard to a human user that provides the human user with raw sensordata, analytical data, and/or predictions or classifications based onsensor data received from the sensor kit. In embodiments, providedherein is a sensor kit system having a backend system that includes ananalytics module that performs analytics tasks on sensor data receivedfrom the sensor kit and having a backend system that includes adashboard module that presents a dashboard to a human user that providesa graphical user interface that allows the user to configure the sensorkit system. In embodiments, provided herein is a sensor kit systemhaving a backend system that includes an analytics module that performsanalytics tasks on sensor data received from the sensor kit and having asensor kit and a backend system that includes a configuration modulethat maintains configurations of the sensor kit and configures a sensorkit network by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit system having a backend system thatincludes an analytics module that performs analytics tasks on sensordata received from the sensor kit and having a sensor kit and a backendsystem that updates a distributed ledger based on sensor data providedby the sensor kit. In embodiments, provided herein is a sensor kitsystem having a backend system that includes an analytics module thatperforms analytics tasks on sensor data received from the sensor kit andhaving a sensor kit and a backend system that updates a smart contractdefining a condition that may trigger an action based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes an analyticsmodule that performs analytics tasks on sensor data received from thesensor kit and having a distributed ledger that is at least partiallyshared with a regulatory body to provide information related tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit system having a backend system thatincludes an analytics module that performs analytics tasks on sensordata received from the sensor kit and having sensor kit and a backendsystem that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes an analytics module that performs analytics taskson sensor data received from the sensor kit and having a sensor, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes a control module that provides commands to a deviceor system in an industrial setting to take remedial action in responseto a particular issue being detected. In embodiments, provided herein isa sensor kit system having a backend system that includes a controlmodule that provides commands to a device or system in an industrialsetting to take remedial action in response to a particular issue beingdetected and having a backend system that includes a dashboard modulethat presents a dashboard to a human user that provides the human userwith raw sensor data, analytical data, and/or predictions orclassifications based on sensor data received from the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a control module that provides commands to a deviceor system in an industrial setting to take remedial action in responseto a particular issue being detected and having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides a graphical user interface that allows the user toconfigure the sensor kit system. In embodiments, provided herein is asensor kit system having a backend system that includes a control modulethat provides commands to a device or system in an industrial setting totake remedial action in response to a particular issue being detectedand having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a control module that provides commands to a deviceor system in an industrial setting to take remedial action in responseto a particular issue being detected and having a sensor kit and abackend system that updates a distributed ledger based on sensor dataprovided by the sensor kit. In embodiments, provided herein is a sensorkit system having a backend system that includes a control module thatprovides commands to a device or system in an industrial setting to takeremedial action in response to a particular issue being detected andhaving a sensor kit and a backend system that updates a smart contractdefining a condition that may trigger an action based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes a control modulethat provides commands to a device or system in an industrial setting totake remedial action in response to a particular issue being detectedand having a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein is asensor kit system having a backend system that includes a control modulethat provides commands to a device or system in an industrial setting totake remedial action in response to a particular issue being detectedand having sensor kit and a backend system that updates a smartcontract, wherein the smart contract verifies one or more conditions putforth by a regulatory body with respect to compliance with a regulationor regulatory action. In embodiments, provided herein is a sensor kitsystem having a backend system that includes a control module thatprovides commands to a device or system in an industrial setting to takeremedial action in response to a particular issue being detected andhaving a sensor, an edge device, and a gateway device that communicateswith a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides the human user with raw sensor data, analyticaldata, and/or predictions or classifications based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides the humanuser with raw sensor data, analytical data, and/or predictions orclassifications based on sensor data received from the sensor kit andhaving a backend system that includes a dashboard module that presents adashboard to a human user that provides a graphical user interface thatallows the user to configure the sensor kit system. In embodiments,provided herein is a sensor kit system having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides the human user with raw sensor data, analytical data,and/or predictions or classifications based on sensor data received fromthe sensor kit and having a sensor kit and a backend system thatincludes a configuration module that maintains configurations of thesensor kit and configures a sensor kit network by transmittingconfiguration requests to sensor devices, generating device recordsbased on responses to the configuration requests, and/or adding newsensors to the sensor kit. In embodiments, provided herein is a sensorkit system having a backend system that includes a dashboard module thatpresents a dashboard to a human user that provides the human user withraw sensor data, analytical data, and/or predictions or classificationsbased on sensor data received from the sensor kit and having a sensorkit and a backend system that updates a distributed ledger based onsensor data provided by the sensor kit. In embodiments, provided hereinis a sensor kit system having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides the humanuser with raw sensor data, analytical data, and/or predictions orclassifications based on sensor data received from the sensor kit andhaving a sensor kit and a backend system that updates a smart contractdefining a condition that may trigger an action based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides the humanuser with raw sensor data, analytical data, and/or predictions orclassifications based on sensor data received from the sensor kit andhaving a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein is asensor kit system having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides the humanuser with raw sensor data, analytical data, and/or predictions orclassifications based on sensor data received from the sensor kit andhaving sensor kit and a backend system that updates a smart contract,wherein the smart contract verifies one or more conditions put forth bya regulatory body with respect to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kitsystem having a backend system that includes a dashboard module thatpresents a dashboard to a human user that provides the human user withraw sensor data, analytical data, and/or predictions or classificationsbased on sensor data received from the sensor kit and having a sensor,an edge device, and a gateway device that communicates with acommunication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides a graphical user interface that allows the userto configure the sensor kit system. In embodiments, provided herein is asensor kit system having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides agraphical user interface that allows the user to configure the sensorkit system and having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides a graphical user interface that allows the userto configure the sensor kit system and having a sensor kit and a backendsystem that updates a distributed ledger based on sensor data providedby the sensor kit. In embodiments, provided herein is a sensor kitsystem having a backend system that includes a dashboard module thatpresents a dashboard to a human user that provides a graphical userinterface that allows the user to configure the sensor kit system andhaving a sensor kit and a backend system that updates a smart contractdefining a condition that may trigger an action based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a backend system that includes a dashboardmodule that presents a dashboard to a human user that provides agraphical user interface that allows the user to configure the sensorkit system and having a distributed ledger that is at least partiallyshared with a regulatory body to provide information related tocompliance with a regulation or regulatory action. In embodiments,provided herein is a sensor kit system having a backend system thatincludes a dashboard module that presents a dashboard to a human userthat provides a graphical user interface that allows the user toconfigure the sensor kit system and having sensor kit and a backendsystem that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit system having a backendsystem that includes a dashboard module that presents a dashboard to ahuman user that provides a graphical user interface that allows the userto configure the sensor kit system and having a sensor, an edge device,and a gateway device that communicates with a communication network onbehalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a sensorkit and a backend system that includes a configuration module thatmaintains configurations of the sensor kit and configures a sensor kitnetwork by transmitting configuration requests to sensor devices,generating device records based on responses to the configurationrequests, and/or adding new sensors to the sensor kit. In embodiments,provided herein is a sensor kit system having a sensor kit and a backendsystem that includes a configuration module that maintainsconfigurations of the sensor kit and configures a sensor kit network bytransmitting configuration requests to sensor devices, generating devicerecords based on responses to the configuration requests, and/or addingnew sensors to the sensor kit and having a sensor kit and a backendsystem that updates a distributed ledger based on sensor data providedby the sensor kit. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit andhaving a sensor kit and a backend system that updates a smart contractdefining a condition that may trigger an action based on sensor datareceived from the sensor kit. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system that includesa configuration module that maintains configurations of the sensor kitand configures a sensor kit network by transmitting configurationrequests to sensor devices, generating device records based on responsesto the configuration requests, and/or adding new sensors to the sensorkit and having a distributed ledger that is at least partially sharedwith a regulatory body to provide information related to compliance witha regulation or regulatory action. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system that includesa configuration module that maintains configurations of the sensor kitand configures a sensor kit network by transmitting configurationrequests to sensor devices, generating device records based on responsesto the configuration requests, and/or adding new sensors to the sensorkit and having sensor kit and a backend system that updates a smartcontract, wherein the smart contract verifies one or more conditions putforth by a regulatory body with respect to compliance with a regulationor regulatory action. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system that includes aconfiguration module that maintains configurations of the sensor kit andconfigures a sensor kit network by transmitting configuration requeststo sensor devices, generating device records based on responses to theconfiguration requests, and/or adding new sensors to the sensor kit andhaving a sensor, an edge device, and a gateway device that communicateswith a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a sensorkit and a backend system that updates a distributed ledger based onsensor data provided by the sensor kit. In embodiments, provided hereinis a sensor kit system having a sensor kit and a backend system thatupdates a distributed ledger based on sensor data provided by the sensorkit and having a sensor kit and a backend system that updates a smartcontract defining a condition that may trigger an action based on sensordata received from the sensor kit. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system that updatesa distributed ledger based on sensor data provided by the sensor kit andhaving a distributed ledger that is at least partially shared with aregulatory body to provide information related to compliance with aregulation or regulatory action. In embodiments, provided herein is asensor kit system having a sensor kit and a backend system that updatesa distributed ledger based on sensor data provided by the sensor kit andhaving sensor kit and a backend system that updates a smart contract,wherein the smart contract verifies one or more conditions put forth bya regulatory body with respect to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system that updates adistributed ledger based on sensor data provided by the sensor kit andhaving a sensor, an edge device, and a gateway device that communicateswith a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having a sensorkit and a backend system that updates a smart contract defining acondition that may trigger an action based on sensor data received fromthe sensor kit. In embodiments, provided herein is a sensor kit systemhaving a sensor kit and a backend system that updates a smart contractdefining a condition that may trigger an action based on sensor datareceived from the sensor kit and having a distributed ledger that is atleast partially shared with a regulatory body to provide informationrelated to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit system having a sensor kitand a backend system that updates a smart contract defining a conditionthat may trigger an action based on sensor data received from the sensorkit and having sensor kit and a backend system that updates a smartcontract, wherein the smart contract verifies one or more conditions putforth by a regulatory body with respect to compliance with a regulationor regulatory action. In embodiments, provided herein is a sensor kitsystem having a sensor kit and a backend system that updates a smartcontract defining a condition that may trigger an action based on sensordata received from the sensor kit and having a sensor, an edge device,and a gateway device that communicates with a communication network onbehalf of the sensor kit.

In embodiments, provided herein is a sensor kit system having adistributed ledger that is at least partially shared with a regulatorybody to provide information related to compliance with a regulation orregulatory action. In embodiments, provided herein is a sensor kitsystem having a distributed ledger that is at least partially sharedwith a regulatory body to provide information related to compliance witha regulation or regulatory action and having sensor kit and a backendsystem that updates a smart contract, wherein the smart contractverifies one or more conditions put forth by a regulatory body withrespect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit system having a distributedledger that is at least partially shared with a regulatory body toprovide information related to compliance with a regulation orregulatory action and having a sensor, an edge device, and a gatewaydevice that communicates with a communication network on behalf of thesensor kit.

In embodiments, provided herein is a sensor kit system having sensor kitand a backend system that updates a smart contract, wherein the smartcontract verifies one or more conditions put forth by a regulatory bodywith respect to compliance with a regulation or regulatory action. Inembodiments, provided herein is a sensor kit system having sensor kitand a backend system that updates a smart contract, wherein the smartcontract verifies one or more conditions put forth by a regulatory bodywith respect to compliance with a regulation or regulatory action andhaving a sensor, an edge device, and a gateway device that communicateswith a communication network on behalf of the sensor kit.

In embodiments, provided herein is a sensor kit having a sensor, an edgedevice, and a gateway device that communicates with a communicationnetwork on behalf of the sensor kit.

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having,” as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platforms. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable the execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more threads. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions and programs as described herein andelsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,Internet server, intranet server, cloud server, and other variants suchas a secondary server, host server, distributed server and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of programs across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, Internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for the execution of methods asdescribed in this application may be considered as a part of theinfrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of programs across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more locations without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other network types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic book readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g., USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;other computer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flowcharts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flowchart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable devices, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium. The computerexecutable code may be created using a structured programming languagesuch as C, an object oriented programming language such as C++, or anyother high-level or low-level programming language (including assemblylanguages, hardware description languages, and database programminglanguages and technologies) that may be stored, compiled or interpretedto run on one of the above devices, as well as heterogeneouscombinations of processors, processor architectures, or combinations ofdifferent hardware and software, or any other machine capable ofexecuting program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitations of ranges ofvalues herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein may be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate the disclosure and does not pose a limitation on the scope ofthe disclosure unless otherwise claimed. No language in thespecification should be construed as indicating any non-claimed elementas essential to the practice of the disclosure.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will understand and appreciate theexistence of variations, combinations, and equivalents of the specificembodiment, method, and examples herein. The disclosure should thereforenot be limited by the above-described embodiment, method, and examples,but by all embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

What is claimed is:
 1. A method for monitoring an industrial settingusing a sensor kit having a plurality of sensors and an edge deviceincluding a processing system, comprising: receiving, by the processingsystem, reporting packets from one or more respective sensors of theplurality of sensors, wherein each reporting packet includes routingdata and one or more instances of sensor data; generating, by theprocessing system, a block of media content frames, wherein each mediacontent frame includes a plurality of frame values, each frame valuebeing indicative of a respective instance of sensor data; compressing,by the processing system, the block of media content frames using amedia codec to obtain a compressed block; generating, by the processingsystem, one or more server kit packets based on the compressed block;and transmitting, by the processing system, the one or more server kitpackets to a backend system via a public network.
 2. The method of claim1, wherein the sensor kit includes a gateway device configured toreceive sensor kit packets from the edge device via a wiredcommunication link and transmit the sensor kit packets to the backendsystem via the public network on behalf of the edge device.
 3. Themethod of claim 2, wherein the gateway device includes a satelliteterminal device that is configured to transmit the sensor kit packets toa satellite that routes the sensor kits to the public network.
 4. Themethod of claim 2, wherein the gateway device includes a cellularchipset that is pre-configured to transmit sensor kit packets to acellphone tower of a preselected cellular provider.
 5. The method ofclaim 1, wherein receiving the reporting packets from the one or morerespective sensors is performed using a first communication device thatreceives reporting packets from the plurality of sensors via aself-configuring sensor kit network and transmitting the sensor kitpackets to the backend system is performed using a second communicationdevice.
 6. The method of claim 5, wherein the second communicationdevice of the edge device is a satellite terminal device that isconfigured to transmit the sensor kit packets to a satellite that routesthe sensor kits to the public network.
 7. The method of claim 5, furthercomprising: capturing, by the plurality of sensors, sensor data; andtransmitting, by the plurality of sensors, the sensor data to the edgedevice via the self-configuring sensor kit network.
 8. The method ofclaim 7, wherein transmitting the sensor data via the self-configuringsensor kit network includes directly transmitting, by each sensor of theplurality of sensors, instances of sensor data with the edge deviceusing a short-range communication protocol, wherein the self-configuringsensor kit network is a star network.
 9. The method of claim 8, furthercomprising initiating, by the processing system, configuration of theself-configuring sensor kit network.
 10. The method of claim 7, whereinthe self-configuring sensor kit network is a mesh network and eachsensor of the plurality of sensors includes a communication device. 11.The method of claim 10, further comprising: establishing, by thecommunication device of each sensor of the plurality of sensors, acommunication channel with at least one other sensor of the plurality ofsensors; receiving, by at least one sensor of the plurality of sensors,instances of sensor data from one or more other sensors of the pluralityof sensors; and routing, by the at least one sensor of the plurality ofsensors, the received instances of the sensor data towards the edgedevice.
 12. The method of claim 7, wherein the self-configuring sensorkit network is a hierarchical network and the sensor kit includes one ormore collection devices.
 13. The method of claim 12, further comprising:receiving, by at least one collection device of the plurality ofcollection devices, reporting packets from one or more sensors of theplurality of sensors; and routing, by the at least one collection deviceof the plurality of collection devices, the reporting packets to theedge device.
 14. The method of claim 1, further comprising storing, byone or more storage devices of the edge device, instances of sensor datacaptured by the plurality of sensors of the sensor kit.
 15. The methodof claim 1, wherein the edge device further comprises one or morestorage devices that store a model data store that stores one or moremachine-learned models that are each trained to predict or classify acondition of an industrial component of the industrial setting and/orthe industrial setting based on a set of features that are derived frominstances of sensor data captured by one or more of the plurality ofsensors.
 16. The method of claim 15, further comprising: generating, bythe processing system, a feature vector based on one or more instancesof sensor data received from one or more sensors of the plurality ofsensors; inputting, by the processing system, the feature vector to themachine-learned model to obtain a prediction or classification relatingto a condition of a particular industrial component of the industrialsetting or the industrial setting and a degree of confidencecorresponding to the prediction or classification; and selecting themedia codec used to compress the block of media content frames based onthe classification or prediction.
 17. The method of claim 16, whereinselecting the media codec includes: selecting a lossy codec in responseto obtaining one or more predictions or classifications relating toconditions of respective industrial components of the industrial settingand the industrial setting that collectively indicate that there arelikely no issues relating to any industrial component of the industrialsetting and the industrial setting.
 18. The method of claim 16, whereinselecting the media codec includes: selecting a lossless codec inresponse to obtaining a prediction or classification relating to acondition of a particular industrial component or the industrial settingthat indicates that there is likely an issue relating to the particularindustrial component or the industrial setting.
 19. The method of claim11, further comprising: generating, by the processing system, a featurevector based on one or more instances of sensor data received from oneor more sensors of the plurality of sensors; inputting, by theprocessing system, the feature vector to the machine-learned model toobtain a prediction or classification relating to a condition of aparticular industrial component of the industrial setting or theindustrial setting and a degree of confidence corresponding to theprediction or classification; and selectively storing, by the processingsystem, the one or more instances of sensor data in the storage deviceof the edge device based on the prediction or classification.
 20. Themethod of claim 19, wherein selectively storing the one or moreinstances of sensor data in the storage device includes: storing the oneor more instances of sensor data in the storage device with an expirysuch that the one or more instances of sensor data are purged from thestorage device in accordance with the expiry, wherein storing the one ormore instances of sensor data in the storage device with an expiry isperformed in response to obtaining one or more predictions orclassifications relating to conditions of respective industrialcomponents of the industrial setting and the industrial setting thatcollectively indicate that there are likely no issues relating to anyindustrial component of the industrial setting and the industrialsetting.
 21. The method of claim 19, wherein selectively storing the oneor more instances of sensor data in the storage device includes: storingthe one or more instances of sensor data in the storage deviceindefinitely in response to obtaining a prediction or classificationrelating to a condition of a particular industrial component or theindustrial setting that indicates that there is likely an issue relatingto the particular industrial component or the industrial setting. 22.The method of claim 1, wherein generating the block of media contentframes includes: normalizing, by the processing system, for eachinstance of sensor data that is to be included in a media content frame,the instance of sensor data into a respective normalized media contentframe value that is within of range of media content frame values thatare permitted by an encoding standard corresponding to the media contentframe; and embedding, by the processing system, each respectivenormalized media content frame value into the media content frame. 23.The method of claim 22, wherein each media content frame is a videoframe comprising a plurality of pixels and the respective normalizedmedia frame values are pixel values.
 24. The method of claim 23, whereinembedding each respective normalized media content frame value into themedia content frame includes: determining, by the processing system, apixel of the plurality of pixels corresponding to the respectivenormalized media content frame based on a mapping that maps respectivesensors of the plurality of sensors to respective pixels of theplurality of pixels; and setting a value of the determined pixel equalto the respective normalized media content frame value.
 25. The methodof claim 23, wherein the codec is an H.264/MPEG-4 codec.
 26. The methodof claim 23, wherein the codec is an H.265/MPEG-H codec.
 27. The methodof claim 23, wherein the codec is an H.263/MPEG-4 codec.
 28. The methodof claim 1, wherein the plurality of sensors includes a first set ofsensors of a first sensor type and a second set of sensors of a secondsensor type.